A Building Controls OpenAI Gym Environment - 2nd-Order Model Train Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Building Controls OpenAI Gym Environment - 1st-Order Model Train Demo\n",
    "**(C) Vasken Dermardiros**\n",
    "\n",
    "Supplementary work to accompany \"A Simplied Building Controls Environment with a Reinforcement Learning Application\" paper submitted to IBPSA Rome 2019 Conference. Paper authors: Vasken Dermardiros, Scott Bucking and Andreas K. Athienitis.\n",
    "\n",
    "Please consult the following links to be more accustomed to how OpenAI Gym and Tensorforce work:\n",
    "+ OpenAI Documentation: https://gym.openai.com/docs\n",
    "+ Creating a new OpenAI Env: https://github.com/openai/gym/tree/master/gym/envs#how-to-create-new-environments-for-gym\n",
    "+ OpenAI Env Wiki: https://github.com/openai/gym/wiki/Environments\n",
    "+ Simple example: https://github.com/MartinThoma/banana-gym\n",
    "+ OpenAI Baselines: https://github.com/openai/baselines\n",
    "+ Tensorforce: https://github.com/tensorforce/tensorforce\n",
    "\n",
    "Other info:\n",
    "+ MuJoCo: https://www.roboti.us/index.html\n",
    "+ https://jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/\n",
    "+ https://python-packaging.readthedocs.io/en/latest/minimal.html\n",
    "+ https://ray.readthedocs.io/en/latest/rllib.html\n",
    "+ https://github.com/ray-project/ray/tree/master/python/ray/rllib\n",
    "\n",
    "\n",
    "To install, go to gym-BuildingControls folder and run the command:\n",
    "\n",
    "    pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make notebook full width\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2nd-Order Model: Changes to Make to BuildingControls_env.py file\n",
    "To run the 2nd-order model, comment this line:\n",
    "\n",
    "    self.bldg = 0 # 1st-order model: direct conditionning of room air\n",
    "\n",
    "and uncomment the following line:\n",
    "\n",
    "    # self.bldg = 1 # 2nd-order model: radiant slab system to condition room air node\n",
    "    \n",
    "in the `BuildingControls_env.py` file.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vasken\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building: 1, 2nd-order model.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 0\n",
      "Settings mode: 1\n",
      "Building: 1, 2nd-order model.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 0\n",
      "Settings mode: 1\n"
     ]
    }
   ],
   "source": [
    "# from /tensorforce/examples/quickstart.py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm # progress bar\n",
    "\n",
    "from tensorforce.core.preprocessors import Preprocessor\n",
    "from tensorforce.execution import Runner\n",
    "from tensorforce.contrib.openai_gym import OpenAIGym\n",
    "\n",
    "# Create an OpenAIgym environment.\n",
    "import gym\n",
    "import gym_BuildingControls\n",
    "environment = OpenAIGym('BuildingControls-v0', visualize=False) # tensorforce\n",
    "env_gym = gym.make('BuildingControls-v0') # openai gym\n",
    "cumsum_curriculum = np.cumsum(env_gym.curriculum)\n",
    "\n",
    "import os\n",
    "if os.name == 'nt':      save_dir = 'C:/RLAgentSaved/'\n",
    "elif os.name == 'posix': save_dir = '/home/vasken/RLAgentSaved/'\n",
    "restore = False    \n",
    "save_interval = 1e10 #int(episodes/10.)\n",
    "report_interval = 500\n",
    "plot_interval = 1e10 # XXX: Don't use\n",
    "plot_test_interval = 1e10 # XXX: Don't use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "matplotlib.rcParams.update({'figure.subplot.left': 0.15, 'figure.subplot.bottom': 0.19})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User-defined classes and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Following are controller classes\n",
    "class agent_constant():\n",
    "    def __init__(self, constant_action):\n",
    "        self.action = constant_action\n",
    "    def act(self, s, deterministic=True):\n",
    "        return self.action\n",
    "\n",
    "class agent_random():\n",
    "    def __init__(self, nA):\n",
    "        self.nA = nA\n",
    "    def act(self, s, deterministic=True):\n",
    "        return np.random.choice(self.nA)\n",
    "    \n",
    "class agent_bangbang():\n",
    "    \"\"\" One stage bang-bang controls\"\"\"\n",
    "    def __init__(self, hvac_levels, heat_on, heat_off, cool_on, cool_off, hvac_off=None):\n",
    "        self.hvac_levels = hvac_levels\n",
    "        self.heat_on, self.heat_off = heat_on, heat_off\n",
    "        self.cool_on, self.cool_off = cool_on, cool_off\n",
    "        self.hvac_off = (self.hvac_levels-1)/2. if (hvac_off == None) else hvac_off\n",
    "    def act(self, s, deterministic=True):\n",
    "        T = s[0]          # room sensor temp\n",
    "        hvac_state = s[1] # hvac state\n",
    "        self.action = 1   # default action: don't change hvac state/setting\n",
    "        if (T < self.heat_on) and (hvac_state == self.hvac_off): self.action = 2 # turn on heating\n",
    "        if (T > self.heat_off) and (hvac_state > self.hvac_off): self.action = 0 # turn off heating\n",
    "        if (T > self.cool_on) and (hvac_state == self.hvac_off): self.action = 0 # turn on cooling\n",
    "        if (T < self.cool_off) and (hvac_state < self.hvac_off): self.action = 2 # turn off cooling\n",
    "        return self.action \n",
    "    \n",
    "class agent_bangbang_2stage():\n",
    "    \"\"\" Two stage bang-bang controls for heating only \"\"\"\n",
    "    def __init__(self, hvac_levels, heat_on, heat_off, heat_on_stage2, heat_off_stage2, hvac_off=None):\n",
    "        self.hvac_levels = hvac_levels\n",
    "        self.heat_on, self.heat_off = heat_on, heat_off\n",
    "        self.heat_on_stage2, self.heat_off_stage2 = heat_on_stage2, heat_off_stage2\n",
    "        self.hvac_off = (self.hvac_levels-1)/2. if (hvac_off == None) else hvac_off\n",
    "    def act(self, s, deterministic=True):\n",
    "        T = s[0]          # room sensor temp\n",
    "        hvac_state = s[1] # hvac state\n",
    "        self.action = 1   # default action: don't change hvac state/setting\n",
    "        if (T < self.heat_on) and (hvac_state == self.hvac_off): self.action = 2 # turn on heating\n",
    "        if (T > self.heat_off) and (hvac_state > self.hvac_off): self.action = 0 # turn off heating\n",
    "        if (T < self.heat_on_stage2): self.action = 2 # turn on heating more\n",
    "        if (T > self.heat_off_stage2) and (hvac_state > self.hvac_off): self.action = 0 # turn off stage 2 heating\n",
    "        return self.action \n",
    "\n",
    "class agent_pi():\n",
    "    \"\"\" Proportional-integral control with discrete mode output \"\"\"\n",
    "    def __init__(self, Kp, Ki, setpoint, hvac_off, mode='heating', output_deadband=500.):\n",
    "        self.Kp, self.Ki, self.setpoint = Kp, Ki, setpoint\n",
    "        self.hvac_off = hvac_off\n",
    "        self.mode = mode\n",
    "        self.out_db = output_deadband\n",
    "        self.cum_error = 0\n",
    "    def act(self, s, deterministic=True):\n",
    "        T = s[0]          # room sensor temp (process variable)\n",
    "        hvac_state = s[1] # hvac state\n",
    "        self.action = 1   # default action: don't change hvac state/setting\n",
    "        error = self.setpoint - T # setpoint error\n",
    "        self.cum_error += error   # cumulative error\n",
    "        output = self.Kp*error + self.Ki*self.cum_error # PI raw output\n",
    "        if self.mode == 'heating':\n",
    "            # TODO make this part generic/general\n",
    "            if (output <= 2500-self.out_db) & (hvac_state > self.hvac_off): self.action = 0 # turn off heating (hvac setting 1, off)\n",
    "            if (output >  2500+self.out_db) & (hvac_state <= self.hvac_off): self.action = 2 # turn on heating (hvac setting 2)\n",
    "            if (output <= 7500-self.out_db) & (hvac_state >  2): self.action = 0 # turn down heating (hvac setting 2)\n",
    "            if (output >  7500+self.out_db) & (hvac_state <= 2): self.action = 2 # turn up heating (hvac setting 3)\n",
    "        elif self.mode == 'cooling':\n",
    "            # TODO make this part generic/general\n",
    "            if (output >  -1500+self.out_db) & (hvac_state < self.hvac_off): self.action = 2 # turn off cooling (hvac setting 1, off)\n",
    "            if (output <= -1500-self.out_db) & (hvac_state >= self.hvac_off): self.action = 0 # turn on cooling (hvac setting 0)            \n",
    "        return self.action \n",
    "\n",
    "# Function to plot results    \n",
    "def show_results(agent, perturbation_mode, plot_progression=True, reward_limit=None, plot_T=False, save_prefix=\"\"):\n",
    "    figsize=(10,3)\n",
    "    figext, figdpi = \".png\", 300\n",
    "    tickhrs = 12\n",
    "\n",
    "    # env_gym = gym.make('BuildingControls-v0') # openai gym\n",
    "    env_gym.perturbation_mode = perturbation_mode\n",
    "    env_gym.change_perturbations_mode()\n",
    "    \n",
    "    s = env_gym.reset()\n",
    "    done = False\n",
    "    obs_history = s[0] # only track index 0 state observation = room temperature\n",
    "    hvac_history = s[1] # hvac state [0,1,2,3,4]\n",
    "    act_history = np.array([]) # actions taken -/0/+\n",
    "    Tamb_history = s[15] # ambient temp\n",
    "    T_history = np.empty(env_gym.nN) # all temperatures; agent does not see this, read from \"info\" variable passed @ env.step()\n",
    "    reward_history = np.array([]) # actions taken -/0/+\n",
    "\n",
    "    while not done:\n",
    "        # env.render()\n",
    "        action = agent.act(s, deterministic=True)\n",
    "        # s, done, r = environment.execute(action)\n",
    "        s, r, done, info = env_gym.step(action)\n",
    "\n",
    "        obs_history = np.append(obs_history, s[0])\n",
    "        hvac_history = np.append(hvac_history, s[1])\n",
    "        act_history = np.append(act_history, action-1)\n",
    "        Tamb_history = np.append(Tamb_history, s[15])\n",
    "        T_history = np.vstack((T_history, info['T']))\n",
    "        reward_history = np.append(reward_history, r)\n",
    "        \n",
    "    # llm, lsp, hsp, hlm = env_gym.T_low_limit, env_gym.T_low_sp, env_gym.T_high_sp, env_gym.T_high_limit # setpoint limits\n",
    "    lsp, hsp = env_gym.T_low_sp, env_gym.T_high_sp # setpoint limits\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    plt.plot(obs_history, 'k')\n",
    "    if plot_T: plt.plot(T_history, '--')\n",
    "    plt.xlim((0,env_gym.nT))\n",
    "    ax.set_xticks(np.arange(0,env_gym.nT+1,4*tickhrs))\n",
    "    ax.set_xticklabels(np.arange(0,int(env_gym.nT/4)+1,tickhrs))\n",
    "    ax.set_yticks([lsp, hsp])    \n",
    "    plt.xlabel('Time, h')\n",
    "    plt.ylabel('Temperature, degC')\n",
    "    # plt.ylim()\n",
    "    # plt.plot(np.full(env_gym.nT, hlm), 'b')\n",
    "    plt.plot(np.full(env_gym.nT, hsp), 'b--')\n",
    "    plt.plot(np.full(env_gym.nT, lsp), 'r--')\n",
    "    # plt.plot(np.full(env_gym.nT, llm), 'r')\n",
    "    if save_prefix: plt.savefig(save_prefix+'_Temp'+figext, dpi=figdpi)\n",
    "    plt.show()\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    plt.plot(hvac_history)\n",
    "    # plt.title(\"HVAC State\")\n",
    "    plt.xlim((0,env_gym.nT))\n",
    "    plt.ylim((env_gym.minA,env_gym.maxA))\n",
    "    ax.set_yticks(np.arange(env_gym.maxA+1))\n",
    "    ax.set_yticklabels(np.arange(env_gym.maxA+1))\n",
    "    ax.set_xticks(np.arange(0,env_gym.nT+1,4*tickhrs))\n",
    "    ax.set_xticklabels(np.arange(0,int(env_gym.nT/4)+1,tickhrs))\n",
    "    plt.xlabel('Time, h')\n",
    "    plt.ylabel('HVAC State')\n",
    "    if save_prefix: plt.savefig(save_prefix+'_HVAC'+figext, dpi=figdpi)\n",
    "    plt.show()\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(1,1,1)    \n",
    "    plt.plot(act_history)\n",
    "    plt.xlim((0,env_gym.nT))  \n",
    "    # plt.title(\"Action\")\n",
    "    ax.set_yticks([-1,0,1])\n",
    "    ax.set_yticklabels([\"↓\",\"\",\"↑\"])\n",
    "    plt.yticks(fontsize=20)\n",
    "    ax.set_xticks(np.arange(0,env_gym.nT+1,4*tickhrs))\n",
    "    ax.set_xticklabels(np.arange(0,int(env_gym.nT/4)+1,tickhrs))\n",
    "    plt.xlabel('Time, h')\n",
    "    plt.ylabel('Action')\n",
    "    if save_prefix: plt.savefig(save_prefix+'_Action'+figext, dpi=figdpi)\n",
    "    plt.show()\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(1,1,1)    \n",
    "    plt.plot(Tamb_history)\n",
    "    plt.xlim((0,env_gym.nT))\n",
    "    # plt.title(\"T_ambient\")\n",
    "    ax.set_xticks(np.arange(0,env_gym.nT+1,4*tickhrs))\n",
    "    ax.set_xticklabels(np.arange(0,int(env_gym.nT/4)+1,tickhrs))\n",
    "    plt.xlabel('Time, h')\n",
    "    plt.ylabel('T_ambient, degC')\n",
    "    if save_prefix: plt.savefig(save_prefix+'_TAmbient'+figext, dpi=figdpi)\n",
    "    plt.show()\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(1,1,1)    \n",
    "    plt.plot(reward_history)\n",
    "    plt.xlim((0,env_gym.nT))\n",
    "    # plt.title(\"Reward in episode\")\n",
    "    ax.set_xticks(np.arange(0,env_gym.nT+1,4*tickhrs))\n",
    "    ax.set_xticklabels(np.arange(0,int(env_gym.nT/4)+1,tickhrs))\n",
    "    plt.xlabel('Time, h')\n",
    "    plt.ylabel('Step Reward')\n",
    "    if save_prefix: plt.savefig(save_prefix+'_StepReward'+figext, dpi=figdpi)\n",
    "    plt.show()\n",
    "        \n",
    "    reward_cumulative = np.cumsum(reward_history)\n",
    "    print(\"End reward: %.1f\" % reward_cumulative[-1])\n",
    "    # plt.figure()\n",
    "    # plt.plot(reward_cumulative)\n",
    "    # plt.title(\"Cumulative reward in episode\")\n",
    "\n",
    "    if plot_progression:\n",
    "        # Training progression\n",
    "        # plt.figure(figsize=figsize)\n",
    "        # plt.plot(runner.episode_timesteps, marker='.', linestyle='None', alpha=0.1)\n",
    "        # plt.title(\"Timesteps\")\n",
    "        plt.figure(figsize=figsize)\n",
    "        plt.plot(runner.episode_rewards, marker='.', linestyle='None', alpha=0.1)\n",
    "        if reward_limit != None:\n",
    "            plt.ylim((reward_limit, 0))\n",
    "        plt.xlabel('Episode')\n",
    "        plt.title(\"Rewards\")\n",
    "        if save_prefix: plt.savefig(save_prefix+'_RLTrainRewardEpisodic'+figext, dpi=figdpi)\n",
    "        plt.show()        \n",
    "        \n",
    "# Callback function printing episode statistics\n",
    "def episode_finished(r):\n",
    "    message = \"Finished episode {ep} after {ts} timesteps (reward: {reward})\".format(ep=r.episode, ts=r.episode_timestep, reward=int(r.episode_rewards[-1]))\n",
    "    pbar.set_description(message)\n",
    "    pbar.update(1)\n",
    "    \n",
    "    if (r.episode % save_interval == 0) and (r.episode != 0):\n",
    "        # print(\"Model save checkpoint.\")\n",
    "        r.agent.save_model(directory=save_dir, append_timestep=True)\n",
    "        # r.agent.save_model()\n",
    "    if (r.episode % report_interval == 0) and (r.episode != 0):      \n",
    "        print(\"Average of last 500 rewards: {}\".format(np.mean(r.episode_rewards[-500:])))\n",
    "        print(\"Average of last 100 rewards: {}\".format(np.mean(r.episode_rewards[-100:])))\n",
    "        print(\"Best 5 of last 100 rewards: {}\".format(np.sort(r.episode_rewards[-100:])[-5::]))\n",
    "    if (r.episode % plot_interval == 0) and (r.episode != 0):      \n",
    "        mode = np.searchsorted(cumsum_curriculum, r.episode)\n",
    "        print(\"Mode: %i\" % mode)\n",
    "        show_results(agent, perturbation_mode=mode)\n",
    "    if (r.episode % plot_test_interval == 0) and (r.episode != 0):        \n",
    "        show_results(agent, perturbation_mode=8, plot_progression=False, save_prefix=\"RLHeat_%i\" % r.episode)\n",
    "        show_results(agent, perturbation_mode=9, plot_progression=False, save_prefix=\"RLCool_%i\" % r.episode)        \n",
    "    return True\n",
    "\n",
    "# Function to create RL agent given type and network architecture\n",
    "def create_agent(network='dense_lstm', flavour='quickstart_custom_net'):\n",
    "    # neural network \"brain\"\n",
    "    if (network=='dense_lstm'):\n",
    "        network_spec = [\n",
    "            dict(type='dense', size=64),            \n",
    "            dict(type='internal_lstm', size=64),\n",
    "        ]\n",
    "    elif (network=='dense_2'):\n",
    "        network_spec = [\n",
    "            dict(type='dense', size=64),            \n",
    "            dict(type='dense', size=64),\n",
    "        ]       \n",
    "    elif (network=='dense_3'):\n",
    "        network_spec = [\n",
    "            dict(type='dense', size=64),\n",
    "            dict(type='dense', size=64),            \n",
    "            dict(type='dense', size=64),\n",
    "        ]                  \n",
    "    else:\n",
    "        print(\"Unknown network input!\")\n",
    "        \n",
    "    # create the agent\n",
    "    if (flavour=='quickstart_custom_net'):\n",
    "        from tensorforce.agents import PPOAgent\n",
    "        \"\"\" https://github.com/reinforceio/tensorforce/blob/master/examples/quickstart.py \"\"\"\n",
    "        agent = PPOAgent(\n",
    "            states=environment.states,\n",
    "            actions=environment.actions,\n",
    "            network=network_spec,\n",
    "            # Agent\n",
    "            states_preprocessing=None, #preprocessing_config,\n",
    "            actions_exploration='epsilon_decay', #None,\n",
    "            reward_preprocessing=None,\n",
    "            # MemoryModel\n",
    "            update_mode=dict(\n",
    "                unit='episodes',\n",
    "                batch_size=10, # 10 episodes per update\n",
    "                frequency=10, # Every 10 episodes\n",
    "            ),\n",
    "            memory=dict(\n",
    "                type='latest',\n",
    "                include_next_states=False,\n",
    "                capacity=1000\n",
    "            ),\n",
    "            # DistributionModel\n",
    "            distributions=None,\n",
    "            entropy_regularization=0.01,\n",
    "            # PGModel\n",
    "            baseline_mode='states',\n",
    "            baseline=dict(\n",
    "                type='mlp',\n",
    "                sizes=[64, 64]\n",
    "            ),\n",
    "            baseline_optimizer=dict(\n",
    "                type='multi_step',\n",
    "                optimizer=dict(\n",
    "                    type='adam',\n",
    "                    learning_rate=1e-3\n",
    "                ),\n",
    "                num_steps=20\n",
    "            ),\n",
    "            gae_lambda=0.99,\n",
    "            # PGLRModel\n",
    "            likelihood_ratio_clipping=0.2,\n",
    "            # PPOAgent\n",
    "            step_optimizer=dict(\n",
    "                type='adam',\n",
    "                learning_rate=1e-3\n",
    "            ),\n",
    "            subsampling_fraction=0.2,\n",
    "            optimization_steps=25,\n",
    "            execution=dict(\n",
    "                type='single',\n",
    "                session_config=None,\n",
    "                distributed_spec=None\n",
    "            )\n",
    "        )\n",
    "    elif (flavour=='vpg'):\n",
    "        from tensorforce.agents import VPGAgent\n",
    "        agent = VPGAgent(\n",
    "            states=environment.states,\n",
    "            actions=environment.actions, \n",
    "            network=network_spec, \n",
    "            batched_observe=False, \n",
    "            #batching_capacity=10, \n",
    "            scope='vpg', \n",
    "            execution=None, \n",
    "            variable_noise=None, \n",
    "            states_preprocessing=None, \n",
    "            actions_exploration='epsilon_decay', \n",
    "            reward_preprocessing=None, \n",
    "            update_mode=None, \n",
    "            memory=None, \n",
    "            optimizer={'type':'adam', 'learning_rate':1e-3}, \n",
    "            discount=0.99, \n",
    "            # distributions=None, \n",
    "            # entropy_regularization=None,\n",
    "            # baseline_mode='states', \n",
    "            # baseline={'type':'mlp', 'sizes':[32, 32]},\n",
    "            # baseline_optimizer={'type':'adam', 'learning_rate':1e-3}, \n",
    "            # gae_lambda=None,\n",
    "        )        \n",
    "    else:\n",
    "        print(\"Unknown agent flavour input!\")\n",
    "        \n",
    "    return agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the RL PPO Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "# agent = create_agent(flavour='vpg', network='dense_2')\n",
    "agent = create_agent(flavour='quickstart_custom_net', network='dense_lstm')\n",
    "# agent = create_agent(flavour='quickstart_custom_net', network='dense_2')\n",
    "# agent = create_agent(flavour='quickstart_custom_net', network='dense_3')\n",
    "if restore: agent.restore_model(directory=save_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 50 after 179 timesteps (reward: -549):   1%|▏                       | 50/6000 [00:10<15:33,  6.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 101 after 179 timesteps (reward: -108):   2%|▎                     | 101/6000 [00:17<13:39,  7.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 152 after 359 timesteps (reward: -1453):   3%|▌                    | 152/6000 [00:32<29:08,  3.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 203 after 359 timesteps (reward: -671):   3%|▋                     | 203/6000 [00:45<27:10,  3.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 304 after 95 timesteps (reward: -46):   5%|█▏                      | 304/6000 [00:54<08:29, 11.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 500 after 95 timesteps (reward: -79):   8%|█▉                      | 499/6000 [01:12<09:19,  9.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -523.1933855855668\n",
      "Average of last 100 rewards: -153.37897003889222\n",
      "Best 5 of last 100 rewards: [-55.15761129 -51.7058614  -50.86162202 -49.35304688 -48.74      ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 805 after 95 timesteps (reward: -76):  13%|███▏                    | 804/6000 [01:41<07:30, 11.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 1000 after 191 timesteps (reward: -99):  17%|███▌                 | 1000/6000 [02:11<13:55,  5.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -111.3430068699441\n",
      "Average of last 100 rewards: -159.67215705044117\n",
      "Best 5 of last 100 rewards: [-95.49419421 -94.84502045 -91.19240346 -90.55470618 -90.23846201]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 1500 after 191 timesteps (reward: -93):  25%|█████▎               | 1500/6000 [03:30<12:10,  6.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -134.07102575567203\n",
      "Average of last 100 rewards: -122.2270041737282\n",
      "Best 5 of last 100 rewards: [-88.25008527 -88.06       -88.02805586 -85.06       -82.9       ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 1806 after 191 timesteps (reward: -117):  30%|██████              | 1806/6000 [04:18<09:53,  7.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed lesson. Incrementing perturbation mode.\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 2000 after 383 timesteps (reward: -755):  33%|██████▋             | 2000/6000 [05:07<20:23,  3.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -259.24477116151735\n",
      "Average of last 100 rewards: -585.3136442203523\n",
      "Best 5 of last 100 rewards: [-130.24271364 -112.99620838 -108.77151582 -106.79015099 -105.92085341]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 2500 after 479 timesteps (reward: -197):  42%|████████▎           | 2500/6000 [07:26<16:04,  3.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -284.7529820540942\n",
      "Average of last 100 rewards: -198.74480933472105\n",
      "Best 5 of last 100 rewards: [-68.97487763 -68.28       -67.88116277 -65.85325538 -63.7270806 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 3000 after 191 timesteps (reward: -104):  50%|██████████          | 3000/6000 [09:36<13:16,  3.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -178.77888628346756\n",
      "Average of last 100 rewards: -182.57910433152753\n",
      "Best 5 of last 100 rewards: [-69.95827245 -65.57888628 -63.77903118 -62.30412957 -58.60969962]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 3500 after 383 timesteps (reward: -132):  58%|███████████▋        | 3500/6000 [11:47<12:57,  3.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -169.32375046657893\n",
      "Average of last 100 rewards: -144.4708293066976\n",
      "Best 5 of last 100 rewards: [-52.96       -52.92565101 -52.03100036 -49.33217021 -47.13834133]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 4000 after 287 timesteps (reward: -196):  67%|█████████████▎      | 4000/6000 [13:57<07:25,  4.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -770.8840670927228\n",
      "Average of last 100 rewards: -1894.7874242391733\n",
      "Best 5 of last 100 rewards: [-196.89229063 -149.57621179 -142.79719369 -137.42404155 -134.74555794]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 4500 after 383 timesteps (reward: -201):  75%|███████████████     | 4500/6000 [16:01<06:43,  3.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -532.2164302940222\n",
      "Average of last 100 rewards: -201.80824882942582\n",
      "Best 5 of last 100 rewards: [-68.24863421 -66.32319438 -65.88431649 -51.95293271 -40.05020473]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 5000 after 191 timesteps (reward: -41):  83%|█████████████████▌   | 5000/6000 [18:09<03:54,  4.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -181.11268914166712\n",
      "Average of last 100 rewards: -187.23992442823635\n",
      "Best 5 of last 100 rewards: [-42.660006   -41.22286631 -40.82394394 -39.75088928 -33.82      ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 5500 after 383 timesteps (reward: -133):  92%|██████████████████▎ | 5500/6000 [20:24<02:45,  3.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -146.6911636687433\n",
      "Average of last 100 rewards: -143.4089888571365\n",
      "Best 5 of last 100 rewards: [-45.62365792 -44.56194938 -43.36       -39.47775469 -27.85334742]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished episode 6000 after 191 timesteps (reward: -78): 100%|█████████████████████| 6000/6000 [22:35<00:00,  3.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of last 500 rewards: -133.32608112786247\n",
      "Average of last 100 rewards: -127.11925002761284\n",
      "Best 5 of last 100 rewards: [-39.76       -38.00372724 -35.55885815 -32.72536863 -29.9533692 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "episodes = int(6000)\n",
    "runner = Runner(agent=agent, environment=environment)\n",
    "with tqdm(total=episodes) as pbar: runner.run(episodes=episodes, max_episode_timesteps=None, episode_finished=episode_finished)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Baseline Controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agent_cnst = agent_constant(constant_action=1)\n",
    "# agent_rand = agent_random(env_gym.nA)\n",
    "agent_bang = agent_bangbang(hvac_levels=len(env_gym.heat_cool_levels), heat_on=20.5, heat_off=21.5, cool_on=25, cool_off=23.5, hvac_off=env_gym.heat_cool_off)\n",
    "agent_bang2 = agent_bangbang_2stage(hvac_levels=len(env_gym.heat_cool_levels), heat_on=20.5, heat_off=21.5, heat_on_stage2=19, heat_off_stage2=21, hvac_off=env_gym.heat_cool_off)\n",
    "agent_piheat = agent_pi(Kp=5000., Ki=20., setpoint=21., hvac_off=env_gym.heat_cool_off, mode='heating', output_deadband=2000.)\n",
    "agent_picool = agent_pi(Kp=3000., Ki=10., setpoint=24., hvac_off=env_gym.heat_cool_off, mode='cooling', output_deadband=1000.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heating Mode Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "perturbation_mode = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print('Constant Agent')\n",
    "# show_results(agent_cnst, perturbation_mode, plot_progression=False,)# plot_T=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Random Agent')\n",
    "# show_results(agent_rand, perturbation_mode, plot_progression=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print('BangBang Agent 2 stage')\n",
    "# show_results(agent_bang2, perturbation_mode, plot_progression=False, save_prefix=\"BangBangHeat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Proportional-Integral Agent: Heating Mode')\n",
    "# agent_piheat.cum_error = 0 # reset error\n",
    "# show_results(agent_piheat, perturbation_mode, plot_progression=False, save_prefix=\"PIHeat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RL Agent\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf601eacf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf66117d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf66275400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf6630ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf6616a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End reward: -360.4\n"
     ]
    }
   ],
   "source": [
    "print('RL Agent')\n",
    "# show_results(agent, perturbation_mode, plot_progression=False, save_prefix=\"RLHeat\") # plot_T=True)\n",
    "show_results(agent, perturbation_mode, plot_progression=False, save_prefix=\"RLHeat\", plot_T=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cooling Mode Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "perturbation_mode = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('BangBang Agent')\n",
    "# show_results(agent_bang, perturbation_mode, plot_progression=False, save_prefix=\"BangBangCool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Proportional-Integral Agent: Cooling Mode')\n",
    "# agent_picool.cum_error = 0 # reset error\n",
    "# show_results(agent_picool, perturbation_mode, plot_progression=False, save_prefix=\"PICool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RL Agent\n",
      "Loaded perturbations.\n",
      "Perturbation mode: 11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf56c072e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fPL2vfbLebPLSpz6Sz7/yKQVGlc9e2yxgr20WlL7eKujpa5S7X+Tu72DyDsPnA2fECEz6M0xzTcUco0TWVBsxmg7NgveNmDUNvWrFkOot5zEHPpytViypJqb9ylvgHVO/5+RG05M4vqQ3M7ZkdTKzJxJas94IzAdOKToo6d8wzZoec4wSWdPoRALirFXgCXu8kU7iMPF/THT/r0e8XXy2WmMljSeamPar3mgOPEEJ5+TZb9/xRr6BQyUtuY5yM1vPzPY3s/OAK4FXAF8HNnf3fWIGKLNTqw3P4HH1hFpBqq51YS+6xWKyJWvwiUMtK9xOtSW3nlBC2jI3WrIGu737nf+vkXMKHEnLTBNEPwt4C/DqbNmfAx9z97NnszIzmwc8PvtzBNjCzJ4G3OXuN87mPWVqY0M011Qj+3Y3phNIdGO19q60WmHv20roU2iNHEs8YWgk2HLb6kpLtfWvX3mHKohprM8BPevN5sTxK8NjpqvaeYQBRz8MbOru+842wcosBP6W/awLHJ79/sk+3lOmMFQ1Wa1WEJ1AoqtFSkAaCdUZDUtNVkott60EJNXEtB/unnsi5JiKaclKZ5+RfGaqyVro7n8tamVZgqa9pATDNGt6ShfoqmuvySpSSolD6gnD5P6eTstt6olpP1q7waDPL6N9DuiZQpen9G6mJGuZmW0x05uoqy89oSYrzYtMp5Qu0FUXuyYrhcSh9X9Mdf9PcX+vck3W5I0Gw1uT1Ww67mnUPEpvZkqylgDd9grLni+uuEMK0e+dLGWaLATWCSS2WC1Z4400LmSQ/t2FrXHhUmqVaH1u44lus36k0lI+WrOJz75X4wnVPEpvZkqydmz73YBzgDcAN0eLSArRb/9/mVonnoSuOZU1UZNV8MU0lQsZpN/1lWZLVpwWzhSksr37OSenNA6d9GamaXUWt/9tZk3gcne/LmpU0rdhqsmqN52xmmGmE0hso5HGQ0pprLPUu75aLWwpzRE4Wks7Me1HKkNm9DN2YYoD2Eo+6RzlUqhhurtQd82UJ1aLRVo1WXG6RIvSiiulXT71xLQfEzVZA05qa3188W0kkihK7wZ/RpQo+h2TpUwpTHkxV9Qi1SulNDVSq4Uo1f2/0WwyOpJWy23qiWk/WvvB2ID3zbHayKxbCieHudF5ctjM5hOr3lFYQa1vTe7pf1xqySpPrBaLlAbYjJVIFqWe4P5e6ZqsRL4A9NWSpe7CoTXTiO+/6XhoHeAYM1vR/qC7v7TowKQ/7RfTFC583dSzb/YSXy1S7U0qt8lD+l1fjQTnoJsLLVmDPg+O9lH4ntLxJb2Z6e7COzv+Pi5WIFKs9rGCRhMfYKPeSO+bfVXFSkBSKS6GtuEIEi3iTrMlq9X6l+Y268fk3YXDW5OV0vElvZnp7sL9ywpEihVrPKQYNJJxeWK1WKRymzy0dX0l2l0YWpfTqq2pJT5Kfj9S6Wob7eNmpJSOL+lNWke6FCbWeEgxpHjRqapYReETxcUJfI6pd32l+KViLPFR8vuRSldbETVZKRxf0ht9YhU1TOPepHjRqarJkb0rXJOVeKtMvZFeDWKsicNTkEpX2+iIzfq4S+n4kt4oyaqoYTppNppNnTxKEv3uwgQ+x9RHfG80faJ7LhWpT0XUj1S62nR34dykJKuihqn5X4Xv5ZkLNVljiQ9HkOK4cCMjhtnkFFdVkkpX22hNNVlzUVpHuhRmuFqy0h9moirmxIjvtbRrslIdF66fIQZSlkpXWz9TnaV0fElv9IlV1MQcdUNwS/Z40wd+e/VcEa0lq5HGhQzS7/pKdVy4YZpUvhepdLXVRmzWQ2SMJ3R8SW90Zauo4WrJSvOiU0WTNVmRJohO4HOsRfo/FiXdlqyRZBPTfqTS1VZIS5Za/IeOkqyKGqpxshIcAbuqJu46LfhimtJFIPV9v57okCWjNUs2Me3H5OTKAx6MtNbPiO/pfImR3qR3pEshaokX/7ZTTVZ5YtVk1ROqGUm9FbeR6JAl1a3JqkBLViKJovROn1hFpf5tvl1dNVmlmZxyJk5L1qAvZDB5IUp13x9vpDlkST9DDKSsVfg+6C9ytWzEd/fet3EqiaL0Tle2ikq9LqVdqt/sq2iiJqvowUgTGfAR1JI1W/1M+5KyVL4A9DNGXUrd8dIbJVkVFav2JoYUJ8ytqljDGzSaTczCeEuDlv7dhWnu75Vtycr2g7EBt5aP9nHspTIMhfROSVZFpd5l0i7FaUaqKtaI7+MJtc6MjBgjlvaI76lsq3b9TPuSsomWrAG3AvVz7KXUUiy9UZJVUalPktsu1VvaqyjWfpHaZ5hy11eY4SC9U29lW7ISuTOv1scX31S6PKV36R3pUohY4yHFUG/6wKe8mCtiTTlTb/jAu2PapZwwNJrOWIK1Nf1M+5KyRiJdbX21ZCUyNZD0Tp9YRdUSr0tpl1orSJW15qiLUZM16O6YdqMjluy+X090QvR+hhhIWTotWbOfuDyVRFF6pySrolpFlsNw0kx1mpGqGu1jeo/p1BOrM6olPLBmatuqparT6rSS7UEnKP3ckJFKoii9U5JVUcM0TpZassoVoysttc8w5YE1U63JCi1ZaSam/UhloNx+hhZRTdbwSu9Il0JMFlmmf9Ic17Q6pYpRFB4+w3ROJ7WEuwtTvbuwNmKMJ7rN+pFKV1s/QziMa8T3oaVPrKJSHyuoXUMjvpcqTktWWnVGSd9d2PSk6tdawtyFaW6zfqTS1TY5pZVqsuYSXdkqathqslK826qqxmpWeAtnPbH5J1Oe7LjRbDKW4MUy5cS0H42mM5LAQLn9lHCkkihK75RkVZTGyZLpxKrJSukCkHIRd6pzdVa5JiuFbrZ+7vhOJVGU3g1+z5MoRiONhxRDqndbVdXoyEjh3cipJQ4pD0fQSKzVryXlOrZ+pPIlrp/ehVQSRemdPrWKGpaWrGbTcSepC3TVzY2WrHS7vlKdu7CyNVmJ3FjT74jvKe4zMjNd2SpqWEZ8n6g1SPCbfVWNjhjjRY/4nthFIOWWrFTn6qyNjCS7zfpRT2Sg3MmbkXo/J6eSKErvlGRVVOuCl/ot2Zpdvny1CLU3qSUOtUQnO242naanub+H5Du9bdavVMoR+hknK5VEUXqnJKuiWnNcpf7NVHfNlC9G7U1qLVljiXZ9NTzd/b02YjQS/1I2G41ExnAb62OcrFQSRend4Pc8iaJ1PKZal9LSaKR70amqsVrx3UKNxCb5TvXuwsZE93g626olDO2R3jbrVypfAGp93IyUSqIovSv9UzOzg8zsejN70MwWm9nOZccwF5jZUNyS3Tqp1xK86FRVjAQklQtZy2ii9UUpt9zGuCEiBY1mM4maz37HyUrp+JL8Sr2ymdlrgSOBzwJPB84HTjWzLcqMY65I9dt8u0bCF52qilEU3khsku9U9/1GIpMVT6Wqg5GmkqBM1mTNbsT3FBJF6V3ZzQfvB45192Pc/Up3PxhYCryj5DjmhNEhqLFQ4Xv5QgJSdOF7GheyllRbcVvbPaWEtKW6LVlp1DOpJWtuGi1rRWa2FrAD8OWOp04HnlVWHHPJ2OgIx56/hBMuvHHQoUyrdb7RtDrlWWt0hD9ds4xtPn5aYe+5YrzBlg+fV9j79WusNsLfb11e6P+xCK39Pc2arBHuX1VPbpv168F6kyc9YoNBhzFRs/j+ky7l0P+5rKfXPlhv8viEji/Jz9zL+eZiZo8EbgGe6+5/bHv848B/ufuTOpY/EDgw+3Nb4IpSAu3dw4Blgw5iCqnGBenGlmpcoNhmI9W4IN3YUo0L0o0t1bhAsc3Gk9y9sKy8tJasNp1ZnU3xGO5+NHA0gJld7O4LS4itZ6nGlmpckG5sqcYFim02Uo0L0o0t1bgg3dhSjQsU22yY2cVFvl+ZbdbLgAawoOPxTYB/lxiHiIiISHSlJVnuvhpYDOzZ8dSehLsMRURERCqj7O7CI4Afm9mFwJ+AtwOPBL4zw+uOjh1YH1KNLdW4IN3YUo0LFNtspBoXpBtbqnFBurGlGhcottkoNK7SCt8nVmh2EHAIsCmhmP197YXwIiIiIlVQepIlIiIiMhekN1iLiIiISAUoyRIRERGJIPkka9ATSpvZLmb2GzO7xczczPZre27MzL5gZpeZ2QNmttTMflLGXIxm9hEzu8jMlpvZHWZ2splt22X5o7P4P1hCbO/Mtsny7OcCM9u7Y5knmtkvzOweM1thZn81s61jx9YRw0ezbXJU22PzzOwbZnazma00s3+a2ftKimdTM/th9nk+aGb/MLPntj1vZvYJM7s1i+1sM9umhLiWZNup8+d/s+d72hcLjKtmZp9qOz9cb2afNrMpb+gp8xjI1reBmX3NzG7IPq/zzWzHjmWiHwfdzmHZ8zPuV1mcvzKzZWZ2n5n92cxeECuuvOdWM1s7O16XZcv9xswe1U9cM8WWPf8pM7sqW+fdZvZ7M3tW2/MPzeK6KtumN5nZt81s45hxdSzbdX/PPvfTsmVe1U9ceWIzs2OnOIf8eYr3WWRmZ5jZ/dm+dr6ZPSxWXNkyXY9DM1tgZj82s9uyz/xSM/uvPOtPOsmyNCaUnkco0H8PsLLjufWA7YHPZP/uA2wOnDbdib5AuwLfIkxJtDtQB840s4d2LpgdQDsCt0aOqeVm4FDCNlkI/AH4lZk9JYvnsYS7S68nxL4t8DHg/pLiw8yeCRwAdM5vcQSwN7AvsDXhs/28me0bOZ6NCNvEsvVvDRwM3N622CHAB7LHd8yeO8PMYs8ZsiPhRpXWz/aEAYRPyp7flZz7YsEOBd4JvBvYinCMvhP4SOeCAzgGAL4H7AW8CdiOMIXYmWa2WRZTWcdBt3MY5NuvTgHWAfYgnIvPA35tZltGiivvufVrwCuB1wM7A/OBU8ys1kdcM8UG8E/CvrYd8BzCZ3iamT0ie/6RwGaEbbsd8N/ALsAJkeMCcu/vHyCMXVmUPLGdyZrnkhe1P2lmzyAcJ2cDz2RyKr7xWHHlPA5/RDgn70P4PH9EGClhlxnX7u7J/gB/AY7peOxfwOcGFM/9wH4zLPNkwgVou5Jjm0c4YF7S8fijCdMZbQ0sAT44oG13F/C27PefAMcPIo5s/RsC12YH1NnAUW3PXQEc3rH8Oe3LRIrps8CfujxvhMnUD2t7bF3gvtZ2LXH7HQbcA6w3zfNT7osR4jgF+GHHYz8ETul4rPRjIPts6sA+HY8vBj6d/V76cdB5DsuzXxGmP3Fgt7ZlRrPP+FUx4ppmmTXOrdlxvJowLVtrmc2BJrBXrG02zTLzs9imXS8hoWgC82PGlWd/J3z5vYkwGLgX9Tl2iw04tvPYnOJ15wOfKTKWHHHNeBxmr9u/47Eb8pxLkm3JsskJpU/veCr1CaXnZ//eXfJ6NyC0TE6sN/vGdwLhpH5lyfG0YqiZ2esIF97zzWwEeAnwj6yp+g4LXU2vLTGso4H/cfc/TPHcecBLzGxzgKwL4GlA7FlzXwb8xcx+ama3m9klZvYuM2vNnP1YwmwJE8eDu68E/kiJx0MWz1uA49x9xTSL/ce+GMl5wG5mtlUW25MJifNvWwsM8BgYBWrAgx2PrwSek8hxAPn2qzuBK4F9LXSn1wjzyt5HaAEoS+e5dQdgjDVjvymLtcxjYi3C9lgOXNJl0fnAKmC646aIWGbc37MWyhMISfTtUy0T0XOy89vVZnaMmW3SFtcmwE7AUjM7z8z+bWbnmtkesYLp4Tg8D3iNmW1sZiNmtg/wcELLXFfJJlmEb081/nPKnX/zn1PzJCE72L4CnOzuN5e8+iMJB/gFbY8dDtzp7t8uORbMbDszu59wUvkO8HJ3v5zwzWke8FHCyXFPwgF/vJm9uIS4DgAeD/yfaRZ5N2E73mhm44RWrEPd/ZTIoT0OOAi4jtDFdCTweUKXBEzu84M+HvYkXJi/12WZqfbFGL4A/JhwghwH/k5o2fpW2zIDOQbc/T7C//9jZrZZ9mXjvwkXkU0Z8HHQZsb9ysPX9j0J3SjLCcf0J4AXuvvSMoKc5ty6gNCa1jnJcCnHhJm9ODvHPQi8D9jT3aecIi4rB/gUoWemHjGsPPv7d4DT3P23XZaJ4TTgjYQu5w8Ai4A/mNna2fOPy/49HPg+8ALgXOD7DfSpAAAIHklEQVR3ZvbUSDHlPQ5fQ2jxW0bY/48HXu/u3ZJqYDATRPcq14TSg5Z9gzgO2Ah4acnrPoJQF/Acd29kjz0X2I/QCjMI/8zWvRGhZuKHZrYrodsQ4NfufkT2+yVmtpCQUERLZszsSYRuuZ09TPM0lYOBZxM+wxsIdRRfNrMl7h6zNWsEuNjdW/VEfzOzJxC2yVFtyw36eDgAuGi6k8tU+2JEryWctN9ASLCeBhxpZte7+/9L4BjYl3CxuJmQDPyVcALfnskvuKUfB9OYdr/KWi+/RWjR2pnQGvdW4OdmtqO73xIzsFmcW8s6Js4i7FsPIxwXJ5nZTp2Jp5mtD5xM6MI7JFYwefb3rLb0qYTuwlK5+4ltf15uZosJ59i9gV8weUx8192/n/3+t+y68XbgHRHCynscfprwOT+PkGi9DPiRme3i7pfmWUGKhmZC6bYm2qcAe7j7nSWu+6uEos/d3f26tqd2I3xjXmpmdTOrE/rqv2Bm0VvZ3H21u1/j7q3E4RLCt71lhFqVf3S85Eog9g0NOxEOlCvatslzgYOyvzcEPgcc4u4nu/tl7n4UcCIQ+460pXTfJrdl/w7seMia8/cBjpnm+en2xVi+BHzZ3U9098vd/ceEGxdaieqgj4Fr3f25hG/Km7v7IkL31vUM9jhol2e/2p3QpfJ6d/+Tu//V3Q8CHgD2jxncDOfW2wi9HZ13npVyTLj7A9k57s/u/hZCcfZb25cxs3nAqdmfL3b3zu7jIuXZ3/cg1Lbd37YMwE/N7LyIsf0Hd7+V8AXkCdlDreS0zGNixuMwu7njYOAAd/+9u1/q7ocDF2WPd5VskuVDMqG0mY0BPyWcBHZz99tmeEmR6z6S8C1+d3e/quPpb2UxPa3t51bgq4QDrWwjwNrZ53oR8KSO559I+FYT068Id4a0b5OLCUlU69vfGP95x02D+MfKn+i+Ta4nXFQmjgczW4fQslDW8bA/oan8xM4nZtgXY1mP7p9VEsdAdjFeamYPIXQF/3rAx0G7PPvVetm/zY7XNol4XOQ4ty4mJDbtsT+KUPA9iGvECNDq+mrVPp1GSARf5O6x757Os78fNsUyEL5EvjFyfGuwMCzDZkwmV0sI8ZZ2TOQ8Dlv7/+yuC7Gq+Iv4IXQHrCZ8O9iaUOtxP/DoEmOYx+TOuAL4ePb7FoTu1l8RmoG3J3wbbP2sGzmubxLqI3bvWO+8Lq9ZQjl3Vn2ecJJ+DCGp+RzhhPzC7PmXZZ/rgYT6qAMIJ8u9B7CPnc2adxeeTbjDcFdC7dF+hO6RgyPHsWO2DQ7LtsmrgXuBd7Ytc2j2mb+CUB9zIuGktEEJ28kId/YeM8VzPe+LBcV0LOGb8N7ZvvZy4A7gK11eU8oxkK1rL+CF2X60J6E19y/AWPZ8KcdBt3NYnv2K0FK0DPg5oavpiYRWxHFg+xhxkfPcCnw7W+Z5hKElzsq2cy3WNiMUsH8aeEb29w6EbuFVwFOy129AqMn7O6Glpj3+tWJ9lrPZ3yno7sIZttk8wlAMO2XH6q7Z9rmZtvMX8F7Cee/V2THx0Ww/e2rE/b/rcUj44v0vws0gi4AtCTVlTXLcQR31JFPED6EYeEm2Ay8Gdil5/btmO2Hnz7HZzjLVc84Mt/wWENd06/1El9fMeMAVFNuxhG8Bqwhj7pxJx63NhOTlakICcxmhK2IQ+9fZrJlkLQB+QDhxrwSuInzLsxJi2Ru4lFBIezWhCN/anjdCwfHSbJlzgG1L2k67ZfvXoiL2xYJi2oAwTtIN2Wd1HaHmbp0urynlGMjW9RrCUCGrss/sKGDDjmWiHwfdzmF59ytCDc/vCHVZywnJYl/JYLe4yHluJYzd9Y0srhWE2qfNY24zQsvGLwmJ6Krs318Dz8jxegd2jfVZzmZ/p7gkq9s2Wzfbf24nJDQ3ZI//x2dFqFu7kdAdfSHwvJj7f57jkJAo/5zQDf0A4Tz9pjzr1wTRIiIiIhEkW5MlIiIiMsyUZImIiIhEoCRLREREJAIlWSIiIiIRKMkSERERiUBJloiIiEgESrJEJElm5mb2qkHHAWBmS8ws9tRKIlIxSrJEpFRZ8tTt59hs0U0Jg0uKiAyl0UEHICJzzqZtv7+YMOF0+2MrAbzEeUBFRGJQS5aIlMrdb2v9APd0Pubu98Ka3YVm9pjs79eZ2TlmttLM/mZmTzGzbc3sfDN7wMzOM7PHtq/PzF5iZovN7EEzu97MPmNma80i9HXM7LtmttzMbjazD/W7LUSk2pRkicgwORz4AmEy4HuAnxDmrjuMMHnrOsDXWwub2V7A8YR5A7cB3gy8ijDHYa/eB1xOmLD4C8AXzWyn2f5HRKT6lGSJyDA5wt1/6+5XAV8hJE7fcPez3P3vhGRqt7blDwO+5O4/cPdr3f0s4FDg7WZmPa77dHc/yt2vcfdvANcAe/T/XxKRqlJNlogMk8vafv939u/lHY+tb2brufsKYAdgkZkd2rbMCLAusABYOst1A9wKbNLD60VkjlGSJSLDZLztd+/y2Ejbv4cDP5vive7oY92tdak3QESmpSRLRKrsr8BW7n7NoAMRkblHSZaIVNkngVPM7AbgJKAObAsscvdDBhqZiFSemrpFpLLc/XfA3oRi+Auznw8DN7aWMbNPmJlP/Q4iIrNn7jq3iMjcZWY/BDZ19+cPOhYRqRZ1F4rInJUN47A7GopBRCJQS5aIiIhIBKrJEhEREYlASZaIiIhIBEqyRERERCJQkiUiIiISgZIsERERkQiUZImIiIhE8P8Bt3C/12AR3MMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf664d3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf66447cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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URKwJQ4f70800PgfgBDSbI6j+JKOojp05Vdw2LQbnXpzwmxjrj4eLE5sy+/c/0m8OltDSbvovzZnxQWx9dC4bHprNtw/OAgxbKorSaVVaIYmh3owMt16/ueSYAIK8XFmX7pgrNmv2F1PZ2MZdM+NsHUq3grxc+eRXU7lyXAT//j6b+z7cR1Obfa8+NrV18Kcv07nile2U17fy0vXj+OMlw0+tNl45LoLB/u686GCJpWI+plavvgi8IoRIBvZgGMI9CbgNQ3d9gAsxzI1Uunhz2wk8XZxY3Mvj365aJ6YPDWJTVjlSSrvaKjCnVWmFDA3xYmykn0mvF0IQGfBTXyM3Zw31Lfb9A1mxnuyyBg4U1vF/l46w6r8ZJ43gwqRBfJZWRHObDncXx2k9I6XkjW05p0aJ2Ss3ZyfjyDNvnlmXyfGKRpbfkvyznwf25B9rj/BBSj63TYvhofOH4X3aiChnJw2/njOUx79IZ+vRSmYNC7ZRpIqtmLREI6V8GrgdGAc8D/zH+PEdUspnjC97FbjMEkE6opZ2HS9syGbNgWIWJUfi6977bttzE0IorGnmeEWjGSO0H8crGknLq2HRhMG9/qXp4+ZMfbPjtw1QzOPT1AK0GsEVNig2vzgpjOZ2Hc+sO+JQI3u2Hasku6yRJTN6VstqC0IIls4awlu3TaSotpmFy7azK8f+Du/sL6g9lYQ9cdnIXyRhna6eEEG4rxvPrs+krcNxvmcU8+hJH7GPpZTTpJQBxsc0KeXKLs83Syn7T5OXPth6tIILX/iRFzYc5aKkUH7bx/5FcxIM75D66/bkqrRCnDSCK8dF9PoaPu7O/aJ/kzW16/T9ciukXafni33FzB8eYlKrGHObOiSQ26bF8O7OPG5/Zw91TY7xffnezjyCvFy4dEzPa1ltZU5CCKvvnY6/hzM3vZHCOjs6sarTS/70ZToh3q5nbE7dyVXrxJ8vG8mh4nr+/X2WlSJU7IXJiZgQwk0IcY0Q4vdCCD/j54YIIUzvxzAAbMos5+Y3dwPw3h2TePmG8X2ePRfu505iqLfDFgCfjU4v+XxvIbOHBRPi0/t+RT5uWuqb1dakqfKrmpjx7EZe2Xzc1qGYTXObjk9TC7jmtZ1UNrZyzQTbdLgXQvDk5SN55qpR7MqpYuGybZyoPGmTWExVVNvMD0fKuG5ipFVOmJpTXLAXX947nRHhPjyx5pDd1Iy9vzOXjKJ6/u/SEWdcCevqwqRQbpgcxf+25LDtaP8+nKX8nKkNXYdiGPj9GvB3DP3DAO6hZ0O/+71XNx83jDp5cJZZ9/pnJwSzJ7eahn626rP1aAVl9a0s6uPJNl93Z+rU1qRJGls7uOu9VMrqW/msnzQL3na0ksn/2MDvVh3kZGsHf70iifOGh9g0psWTovjorinUNLXz5JpDNo3lXD4y9ua63sKjjCzF282ZJy4bQXlDK29uPWHrcCirb+Ff32UzMz6IS3pwWv7/LhnB0BAvfvvJfnX4aAAxdUXsBeA7YBA/Pxm5Bphr7qAc1YGCWnbnVnP79Bizz4ecmxBCu06y/Zj91UH0xafGhpvz+vhLU21Nmkavlzy0cj9Hyxu4eFQoOZUnyekHtYdvbsvBw0XLx0un8P1vZ3HzlGi7qHNKjglgyYxYtmRXkF3WYOtwutXWoefjPfnMSxzEYH/7LHg3xYToABaMHMRrW45TaeMk5rlvs2jT6fnrwqQefR+6uzjx38XjqGtq53f9ZIqAcm6mJmLTgH9JKU/vmJkP2EfrZTvwxrYTeLtquW6i+bdEJkT74+2qZXM/2p6sa2rn+0PmabipivW7J6XkeEUjR0rqOVJSz7PfZvLd4TL+eMkIHrtoOAA/OPhInpZ2HTuOV3FhUihT4gLtIgHr6sYp0bg5a3hrm+1Xarqz/lAplY1t3Dw12tah9NmjFybS0qHnpR+O2iyG/KomvthXxE2To4kJ8uzx148I9+GxixPZmFnOOztyzR+gYnd6Mnytu03uKEBNLMVQY7E2vYQ7pseYVA/QU85OGqYOCWRHPxrrs+ZAEW068zTc9HHXUt/S0a9bfPSUXi/545fpfLS74GefXzRhMHdMj0EIQcIgbzYcKeOuWfbZN8oUO3OqaO3QMzfRtluRZxLg6cLV4wfzaVohjyxIIMgGBwjOZsXOPKIDPZg5NMjWofTZkGAvFk+MNJxUnB5LbC8Sob56ZfMxnDSCX83u/b+p26bFsO1oJX//5ggjwnyYHGe/7USUvjN1Rew74KEuf5ZCCB/gKeAbs0flgN7Zbni3e9v0WIvdY1JsAPnVTZTU9Y++uZ+aseGmj5szOr2kSY05An6aZ/fR7gJumxbDazeN57WbxvPO7RN5+qpRp5LV+cNDSM2rcZiTfd3ZnFmOm7OGyT2Y42ptd8yIpa1Dz/s782wdys9kltazO7eaGydHodH0jzcwvzkvHhethn99Z5nThzkVjezLr2Fffg0HC2tp79KipLCmiVVphVw/MZJBfTh8JITg+evGEhXowT0f7KWwpuncX6Q4LFMTsYeAGUKILMANWAnkAqHAHywTmuNoaGnn490FXDwqjAg/d4vdZ4rxXVFKTrXF7mEtWaUNHCysY1FypFlWsHyMfdpUnZghCes6z+6Jy0ZwYVIYFyaFMSchBG2XCQ/zhw9Cp5dsznbM7UkpJZuyKpg+JMjsdZnmNCTYi/mJIazYlUdLu/28WVixKw8XrYZFNjphagkh3m7cOi2Gtekl5FWZ97Tq1weLmffvLVz5yg6ufGUHl7+8nUWv7aSs3tC56bUtxxECfjV7SJ/v5evuzPJbkmnX6bnrvTS7+r5RzMvUhq7FwFjgWeB/QCrwKDBeStk/m1v1wGdphTS0drBkhuVWwwCGh/ng7aYl5YTjb0++vOkYbs4aszXc9DFuBw/UFhYVDa18sqeAP32ZzqUv/Xye3dkS3bGRfgR6urDBQevEcipPkl/dxBw73Zbs6s6ZsVSdbOPLffYxE7WhpZ0v9hZx2ehw/D1dbB2OWd0+LQatRvCmGevy6lvaeeqrwyRF+PD27RN5+/aJ/P3KJLLLGrjspW2szyjhkz2FLEqOJNxMb8iHBHvx7NWjOVJSz/ZjqqVFf2VyjZiUshl4y/hQulhzoJgRYT6MMXE8T285aQQTYwJIOeHYK2IHCmr56kAx988baraGmz7uhm/lgbgiJqXkhuW7OFreiLerlqQIX/50yXDuNKFDupNGMDcxhG8PldKu0/dqHqotbco0JJBzHGAszNS4QEaE+fDezrxejzwzpy/3FXGyTdcvivRPF+LjxhVjI/gktYAHzxtGgBkSzX9/m0VlYytv3prM6ME//awfH+XPXe+lcveKvWg1gnvMsBrW1bzEEFycNKScqGb+8EFmvbZiH86YiAkhbjH1IlLK98wTjuMpqWtmb34tv1uQYJX7TY4NYGNmOeUNLYR4974GwVaklDy97ggBni4sNWOBeOcIqYF4cjI1r4aj5Y38ZeFIbpoc3eNan/OGh7AqrZA9udVMG+JYBdtbsiuID/Gy2zmDXQkhWJQ8mKe+OszRsgbiB3nbLBYpJe/vymNUhC9jBvvaLA5LumtWHJ+mFbJiVx4PzI/v07XSC+t4f1ceN0+J/lkSBoadijX3zeCxzw+SEOpj9u9FN2cnxkb6OfwbcOXMzvb2d9lpj+XAO/y0KvaO8XMvWzRCO7c+oxQwdEW2hs7TM7sd9B/l5qwKduVU85v58WY9Xdq5NTkQm7p+mlqAh4sTV48f3KuC65nxwbg4aRyujUVzm46UnGpmO8BqWKdLRoehEYZVdFvafaKa7LJGu+m3ZgnDBnkzNyGYd3fk9qm+SkrJE2syCPB05eELun/DHeDpwv9uTj7nKKPemhwXQEZRHY2tA7P0or87YyImpfTufACLgYPATAzF+m7Gj/cDN1gjUHu1LqOUhEHeDAn2ssr9ksJ98HBxcsiCfZ1e8sy6TGICPczewdtngK6INbV18M3BEi4ZFYana0+60fzE01XLlCGBbMx0rERsT241bTo9M+IdZxUvxNuN6UODWL2/2KbNOlek5OPjpuWyMf27DeTSWUOoOtnG53t7X5e3Nr3UuOsx7NTKu7VNig1Ap5ek5dXY5P6KZZlaEPIv4AEp5XYpZYfxsR14EPi3KRcQQjwmhNgjhKgXQlQIIb4SQiSd9pp3hBDytMeunv0nWU95Qwt7cqu5aJR1VsMAtE4aJkT7O1zBvl4vWbbpGFllDfxuQSIuWvPWInm7ddaIDax3jOvSSznZpmNRct9OvZ03PIQTlSc57kBd9rcfr8TZSTDJjttWdOfyMeHkVzexv6DWJvcvb2hhfUYJ10yIxN3Ffk+amsOUuABGRfjyxtYc9PqeJ76tHTqeWX+ExFBvm80uBUNDb61GkJLjWD/3FdOY+tswBujuHHAThqauppgDvIKhS/88oAPY0M3Q8A1AWJfHxSZe3+q+PVSGlHBxD2aJmcOUuECyyxqpPtlm1fuaorG1g1n/3MQ1r+7gi32FtLTryCiq46pXd/D899mcNzyEiy2QuDo7afBwcRpwK2KfphUQHejBxBj/Pl1nnvHU4Q9HyswRllVsP1bJuCh/PFx6txJoKwuSQnHRali93zbbk5/sKaBdJ7lxiu0PDFiaEIKls+LIqTzJhl58b7+3I4+C6mb+eMlwnGzYZ83DRcuowb6qTqyfMjURSwH+K4SI6PyE8eP/ACatWEkpF0gp35ZSZkgp04GbgWBg+mkvbZVSlnZ52O133rr0EoYEexIfYp1tyU6djSt32+Gq2Cd7CoxNZ1v47coDTP7HD1z+8jYKa5p4/toxLL8l2WI1KT5uA2veZH5VE7tyqrlm/OA+/50O9vcgMdTbYdpY1Jxs41BxPTMcsBu8j5sz8xND+PpgCR1dmoFaQ7tOzwcp+cwYGmS1cgpbuygplAg/d17bcrxH28E1J9t4aeNR5iQEMzPe9nWIk2MDOVhYS7NqWt3vmJqI3QkEArlCiFwhRC6Ghq4hwF29vLe38f6nb3rPEEKUCyGyhRDLhRB22SCoqrGVlBPVXJQUZvVi19GD/XBz1rDLzurEdHrJW9tPkBztz9ZH5/LBksnMSQjm9umx/PDQHK4yQ8JwNj7u2gHVR+yzvYUIAVebYUQUwHnDB5GWV0Ntk/2ttJ5uZ04VUsL0oY45+mXh2HAqG1ut/m94fUYpJXUt3D49xqr3tSWtk4a7Z8exN7+WnT0YEffiD0dpbO3g8YuHWzA6002OC6BdJ9mbr+rE+htTG7oeB0YDlwDPY1gJuxgYJaU81st7v4ih2H9nl8+tB24B5gMPA5OAjUKIbptNCSGWCiFShRCpFRXW7Sv7/eEydHpp1fqwTi5aDeOj/NmSXWFXp2i+O1RKYU0zS2bGodEIpg8N4sXF4/i/S0fg62H5Ildf94GzIqbXS1alFTJjaJDZmkfOHx5i6LKfZf89mrcdq8TLVfuLVgKOYk5CCN6uWlbvt25z17e2nyAm0IO5CXb5/tZiFiVHEuLtyn83mjYMPKeikRW78rhuYhTDbNhmpKvkaH80ArU92Q+ZXDEtDb6TUv5XSvmilPJ72ctjP0KI54EZwNVSylPrrFLKj6WUa6SU6VLKr4CLgAQMCWB3Mb0upUyWUiYHB1t36fib9BKiAz0YEdb3OYm9cdOUaPKqTnL1KzvIr7KPOWTLt+YQFeDB+SNs03RwIG1N7sqpoqi22SwD0zuNGWzosr8py/63J3ccq2RKXIDDNaDt5ObsxIVJoazLKLXa96xhPmItt02L6TdzJU3l5uzE3bOHsCun+pwF7zuOV7Lk3VRctRqLtaPoDW83Z0aG+6qC/X7I6j/FhBD/Aa4H5kkpc872WuNopUKgb934zKy4tpltxypZODbCZj14Lh4Vxrt3TKK0voXLl23jhyNlFFQ3UVDddGrumTWl5dWwN7+WO6bH2Kyo1cfdecBsTX6aVoi3m5YFI823IqvRCGYPC2ZLdgW6Xpwws5aC6iZyq5ocrvns6W6ZGkNjawfv7ci1yv3e3p6Lt6uWa/p4wtZRXT8piiAvV17a2P0mTkVDKw9+vI8blqfQrtfz+i3JBHubZ/KHuUyODWBfQa2aO9nPWDURE0K8iKHv2DwpZaa7YZiSAAAgAElEQVQJrw8CIoASS8fWE5/vLURKuGa8+VYjemNmfDCr751OsJcrd76bysx/bmLmPzcx+R8/sDbdun9lb207gY+bts9tFPrCx007IBq6NrS0sy6jhMvGhJt90PWcxBBqm9pt1lrBFDuOG2buOVL/sO6MGuzLvMQQ3th2wuIlBiV1zaxNL+G6iZF49bLfnKNzd3HiV7Pi2Has8hf9uNo69Fz96g7WppfywLyhfP/b2Uy3w4Mgk+MCaevQc8CO/30qPWe1REwIsQy4HcNqWI0QItT48DI+7yWE+JcQYqoQIkYIMQf4CigHvrBWnOcipaE2Z3JsAFGBth+rEhPkyRf3TufFxWN57prRPHfNaCID3Hlne67VYiiobmJdRgk3TI7udVNRc/Bxd6ahpb1X/YIcyTcHS2hp15t1W7LTrPggNAI22/H25PZjVQR7u1r9tLIl3D9vKLVN7azYlWfR+7y/Mw+9lNw6Lcai97F3N06JIsDThZdOqxX7fG8h+dVNvHrTeB66IMHsb3DMZVJMABqBGgDez1hzRezXGE5K/oBhhavz8YjxeR0wClgNZAPvAlnAVCllgxXjPKvUvBpyq5psuvJzOi9XLQvHRrAoOZJFyZHcNDma3bnVZJVa56/t7e25aITg1mm2HR7s4+aMXsLJtv69PflpWiFDgj0ZZ4Eh834eLoyP8rfbLvtSSnYcr2T6kMB+MZpnXJQ/M+ODWP5jDk0W+r5tauvgo935nD9ikEPM5LQkDxctS2bGsjmr4tSqUrtOz7LNxxhjXKG0Z74ezoyL8mdztv0fqFFMZ7VETEopzvB40vh8s7HXWIiU0kVKGS2lvE1KWWCtGE3xaWoBni5OFmlKai6LkiNxcdLwQYpl32UD1Le0s3JPPpeODiPM1zyn93rLx73/d9fPqWgkLa+GRcmRFktELhg5iEPF9aQX1lnk+n2RVdZAZWObXW4b9dZv5sdTdbKND1PyLXL9D1PyqWlqZ+msOItc39HcMjUGPw/nU7ViX+4roqC6mfvnxTtEcj83IZiDhXVUNLTaOhTFTPqciAkhcoQQrwgh+tba2wF0zvW7eFSYXXfzDvB04eJRoXy+t4iTFq49Wbm7gJNtOpbMtP0P+c7B3/2pu35RbTP/23KcgmrDydhVaYVoBFw1LuIcX9l7iydF4eOmNfmovzVtO2rYkulPiVhyTADThgTy2hbzr4q1tOt4/cccpsYFMiHasUZBWYqXq5Y7psey4UgZBwtrWbbpGCPCfJg/3L5XwzrNMbYe2aJWxfoNc6yI/QiMAA6Z4Vp2ba2Z5vpZw01Tomls7WDNAcuNUenQ6Xl7+wmmxAWQFOFrsfuYyrcfDv7+4xfpPL0uk1nPbeK2t3fzaVohs4cFE+LjZrF7+rg5c8eMWL4/XMbh4nqL3ac3th+rJC7I02y90+zFQ+cPo+pkKw+tPGDWGsdVaYWUN7Ry37yhZrtmf3DrtBi83bTc9V4quVVNPDDfMVbDAEaG+xDi7eoQbWYU0/Q5ETNuH84BEvsejn1bZaa5ftYwIdqfxFBvVuzK69FYj55Ym1FKcV0LS2bYfjUMDMX60H+2Jnccq2RzVgV3zx7C/fPiOVxcT0VDK9dNtPyMwNunxeLlquXlTfazKpZT0ciO41UOf1qyO8kxAfzx4uGsP1TKc99lmeWa7To9r205zthIP6YNccwJBJbi6+7M7dNjKatvJTHUmwts1PuwN4QQzEkI5sfsCquPyFIsw6RETAhxS3fd7YUQLkKIWwCklPb11tnMmtt07D5RzSWjrD/SqDeEENw4JZpDxfUcsECtj5SSN7bmEBfkaTcFrv1pa1Kvlzy9LpMIP3cePC+eh84fxvY/zGPdb2ayYKTlf2n4ejhz27QY1qaXkl1m+7My7To9v125H3cXJ+6d2z9Xd+6cEcv1k6J4dfNxPk3te2nsmv3FFNY0c/+8oQ7xM8va7pgeQ1KED3+4KNHhGtzOTQihoaWDvfmqjUV/YOqK2NtAd3tP3sbn+r3M0nr0EocaqXLF2HC8XbU8+PE+jpr5l+me3BoOFtZxx4xYu/kh9lOxvuMnYl8dLCa9qI6HLxh26ii9s5OG4WE+VvuleueMWDxcnHj5DA0wremlH45yoLCOf1w5ikEW3Ja1JSEEf1k4khlDg3j8i3R29aGDuk4vWbb5GMPDfOzmjZK98fNw4ev7Z56quXIk0+OD0GqE2p7sJ0xNxATQ3f5WFGB/R6ss4HCJYcFvZLhtRhr1hrebM+/cMZHGVh1XvrKDDYfLzHJdKSX//i6LIC8XrrZxU9uuOhtVOnpT19YOHc99m8XwMB+uGGu5ovxz8fd04ZapMXx1sJhj5Y02iyMtr4aXNx3j6vGDuXhUmM3isAZnJw3LbhxPVIAHd69I6/UbqPUZpeRUnOTeuUPUalg/5OPmTHKMP5vstM2M0jNnTcSEEOlCiIMYkrAtQoiDXR6HgK3ABmsEamuHi+vxdtMy2N+xioQnRAew5r7pxAZ5ctf7qfxvy/E+X3NjZjkpJ6r5zfx43F3sp/Gh1kmDl6vW4cccvbM9l8KaZh6/2PZbJktmxuKq1fDKJvOuiuVXNfH899nGKRW/fI/X2NpBSk4Vy3/M4YGP9hHu586Tl48wawz2ytfdmbdum4izk4brl6dwvKJnSXCHTs/Lm44RF+zJRUn9O3EdyOYmhJBZ2kBJXbOtQ1H66Fw9GFYZ/zcJ+Abo+hOhDcgFPjN/WPbncEk9I6y4LWRO4X7ufHr3VB759ABPr8tEL+GeOUN6da0OnZ5n1mUSG+TJ4kmWLxrvKR83rUNvTe4+Uc2/vsvivOEhzIy37iD77gR5uXLT5Gje3pHLA/PjiQny7NP1th2t5PWtOfzY5ej97hPV/GVhEi5aDbVNbfzz2yw+3p1P5+HByAB3Xlw8Dm9jDeBAEB3oyUd3TWbx67u4/vVdfHjXFIaaOEngb98c4UhJPctuGG+zua+K5c1NDOHpdZl8mlrIkpmxdt1SSTm7s/4/J6V8CkAIkQuslFJaf5q0HdDpJZklDSyeZP9tK87EzdmJFxePw0kjeHZ9JlqN4K5eNHj8bG8hR8sbee2m8Tg7WX1m/DkZBn87ZiJWWNPEPSvSGOzvwb8XjbV1OKcsnRXH+7vyeGXzMf55zZheX6eotplb3kohxNuNB8+L59rkSD5MyeflTcc4Vt7IFeMi+M/32dQ2t3Pj5GjmJYYwarAvQV72NXjZWoaGePPBkilcv3wXl/x3K/fPG8pds+Jw1Z55FXrlnnze2ZHLnTNiuWS0Wg3rz+JDvIgN8uT577N5YUM28SHeLEoebBc9HZWeMSmFllK+2/mxEMKP07Y0pZTVZo7LrpyoPElzu46R4bbvldUXThrBvxeNoUMv+fvaI3ToJXfOiMVFa1pC1dym4/nvsxkf5ceCkfY5WcDH3dkhV8Sa2jpY+l4abR16lt+SjK+H/az+hPi4cf2kKFbsyuP+efG9HpPzeVohegmf3j311DUeWZBAYpg3j3x6gNS8GsZH+fH+FaMY4UC1mJaUEOrNNw/M4K9fH+Zf32Xz+b4i/nTJcOYMC/nFtnVqbjV/+jKDmfFBPHZRv+8mNOAJIfjy19NJzavmYGEd36SX8OKGo9wx3X4OUCmmMSkRE0JEA68Bc4GuvyE6i/jtp1DIAjoL9UeEOf4vB62ThheuG4teL3l2fSZvbjvB4omRXD1hMP7GX/5OGtHtNtDbO05QVt/KshvG2+0WrY+bM8W1jlcz8YfP0jlSWs9bt000eQvKmu6ePYQPU/J5dctx/nHlqB5/vZSSVXsLmRIX8ItE7tLR4Qwb5M2x8kYuHBmqfomcJszXnVdunMDmrHKeWHOIO95JJSrAgxsmRzE3IYRj5Y0cLKplVWohg/09ePn68WjtcLVaMT9fD2fmDx/E/OGDCPV147HP0ymoaSI6sG8lBIp1mbqp/DbgB9wBFNP9Ccp+61BxHc5Owi5/QfaGs5OGZTeMZ0t2BR+kGLacXj6tGPuF68ZyRZcxOlJKPtlTwLQhgSTH2O+oFB93LZmljrUi9sORMtYcKOah84cx106P0of6unHtxMGs3FPAr2bF9fgH/e4T1eRVNfHAvPhunx82yJthg7zNEWq/NSchhO9+G8j6jFI+SMnnmXWZPLMuEwBnJ8GoCF/+ec0Yu1pNVawnybhjk1FUrxIxB2NqIjYJmCKlzLBkMPbqcHE9wwZ5m7yF5wg0GsHcxBDmJoZQVNvMxszyU12a39x2gpV7Cn6WiB0paSC3qolfze5dkb+1+Lg5Vo1Yc5uOJ9YcIj7Ei7vt/O/2njlD+XxvEZf+dxuPLEjgpinROGkEmaX1fJiST0ldC0nhvowe7Mu4KD/8PFxOfe2qtEI8XZy4aJR9bmk7CletEwvHRrBwbARHyxrYX1BLYqgPw0K9zlo7pvR/w0K9cHYSpBfVqfpAB2NqInYCGJAVs1JKDhfX9+umiBF+7tw8JfrUn+ua23nxh6OU1rUQ6mtonrkuowSNwO5Hgfi4O9PQ2oFeLx1ii+uljUcprGlm5dIpdp/oR/i5880DM/nz6gyeWHOIT9MKcNU6kZZXg4tWw2A/dzYcKUNKcHd24s1bk5k2NIiTrR18k17CpaPD1MkuM4of5E28WkVUjFy1Tgwb5M2h4gHR2rNfMfUn/2+Ap4UQ/XO2yFmUN7RSdbJtQBUPXz4mHCnh64M/DQxfl1HK5NhAAu38BJuPmxYpoaHV/nuJHS1r4PUfc7h6/GAmxznGLMDYIE/eu2MSL98wjpqT7dScbONPlwwn5bH5bHxkDulPLuDjpVOIDHDnzndTScmpYm16CU1tOhYlO+6pY0VxBEnhvmQU1VlsvrBiGaa+PV2NYUUsSwjRCvzst5yUst9mKYeLOzvqO/aJyZ6IC/ZiVIQvq/cXs2RmHEfLGjhW3sitU6PP/cU2dmrwd3M7vu72WysjpeRPX2bg6arl8Ysd64SbEIJLR4dz6ejwXzzn5aplSlwgHyyZwuLXd3L7O3sI9XUjJtCD5Gh/G0SrKANHUoQPK1MLKK5rIcLPsZqPD2SmJmL3WTQKO9Z5YnJ42MDaAlg4Npy/fXOEnIpG1qaXIgR227Kiq1ODv+28hUVqXg0pJ6r5y8KRdr/K2BvB3q58dNcUFr++i5yKkzxywTC7PWmrKP1FUkRnwX6dSsQcSI/7iA00h4rriA70GFBdvcHQUuDva4+w5kAx6zNKmRgdQIgDDFv2PbUiZt9bk6tSC/FwcbKrWZ3mFuLjxod3TeHtHSe4aYr9r6YqiqMbHuaDk0aQUVTnEG+cFQOTq4OFEIOEEI8IIV4VQgQZPzddCBFrufBs73Bxfb/oH9ZTob5uTI4N4P2deWSWNnBhkmP8o/ZxN7y36FwRa27TUX2yzZYh/UJTWwdfHyzmklFheLr27+L1UF83Hrto+M9OUCqKYhluzk4MDfYio0gV7DsSkxIxIcQEIAu4EbgT6MxMzgf+bpnQbK++pZ3cqqYBmYgBLBwbQZUxiXGYRMztpxqxprYOrn51B7P/uYkfjpTZOLKfrEsv5WSbjmsm9N/VMEVRbCMpwpcMY22z4hhMXRH7F/CilHIc0Nrl898C080elR2QUvLk6kMATBsaZONobOOipFCcnQTjovwId5B6g85i/brmdh7+5ACZpfWE+Liy5L1Ulm06duo0UV1TO3tyq9l5vIqdx6vYfaKatg69VWL8NK2A6EAPJsXab2NcRVEcU1KEDxUNrZTXD8jR0A7J1H2RCRhWwk5XAth3Y6leWr41h8/3FfHw+cOYMEBPe/l5uPDcNWN6PVvQFrxdtQhhaEpbUtfCHy8ezk1Tonn0s4M8920W3x8uo6apjbyqpl987eTYAN6+faJFe13lVzWxK6eah89XxeuKophfZ8F+elEd8x2grlcxPRFrBrrLRhKBclMuIIR4DLgKSMCwqrYLeKxrt35h+M30BLDUeL8U4F4p5SET4zSLTVnlPL0uk0tGhXHfvAHXOu1nunbXdwQajcDLVUtJXQtXjYtgycxYhBD8d/FYksJ9WJVWyIgwH66bGMnwMB/cjN3Is0rr+cvXh1nybipv3TYRN2fLdClftbcQIeAqtS2pKIoFjAjzQQjDqKP5w/vHOsnh4nrKuqzwjRrsS1A/Om3ekz5iTwghFhn/LIUQMcCzwGcmXmMO8AqwB8Ow8L8AG4QQI6SU1cbXPAo8DNyGoSbtz8D3QogEKWWDiffpk4yiOh74aB/DQ314btFotWrhgMJ93XEPduIfV4069f+fEIJfzR5yxhFNU4cE4u3mzCOrDnDXe6ksvyXZ7MlYh07Pyj35zIoPVkfLFUWxCE9XLXFBnmT0kw77FQ2tLFy2jXbdT01qQ7xdWfmrqcQG9Y+ZmqbWiD0CBAAVgAewDTgG1AJ/MuUCUsoFUsq3pZQZUsp04GYgGGONmXE17EHgGSnlZ8aVslsBb+AG0/+Teu+bgyUsem0nni5alt+arMaxOKiPlk7h46VTepxIXT1hMM9eNZqtRyu5Z0UarR06s8a1MbOcsvpWbpgcZdbrKoqidJUU4cuhfnJy8puDxbTrJP+7eQJf3judd26fSIdecsPyXeR3U2LiiEztI1YPzBBCzAPGY0jg9kopN/Th3t7G69QY/xwLhALfdblvsxDiR2Aa8L/TLyCEWIphG5OoqF/+cqtraueprw/R3KYzvh6mxAVyxbiIU6frAPR6yX82ZPPSxmOMj/LjtZsnEOKt9tYdVYBn71slXDsxkg695PEv0rn3g728cuMEs82A/HB3PoN8XJnfj+eWKopie6MH+7F6fzFFtc0Ov/q++kAxw8N8ftYXbcWdk7nhjV1cv3yXcaSa49Qxd6dHSz5Syo3ARjPd+0VgP7DT+OfOv+XT+wyUAd0WKkkpXwdeB0hOTv7FcK13duTy+d4i4kO8EAJa2vWsTS/l6bWZLBwbTpivO+lFtRworKOioZVrkwfz1yuScNVapj5IcQw3TI5Cp9fzf6sPcf9He3n5hvHopSSrtIGapnZmDA3CqYcDxQuqm9iSXcH9c4eidbLv4d6Koji2eYkh/PXrw3x/qJTbpjtuq8/8qib25dfyh4t+PgZuRLiPIRlbvovLX97GtRMjuXFSNFGBjpmQnTERE0I8BLwipWwxfnxGUsrne3JTIcTzwAxghpTy9P2f0xMq0c3nfqGotvlnf25p1/H+rlzmJYbw1m0TT33+YGEtH6bks3p/MS0dOoYEezFzaBCzE4K5fEy4qglTALh5agwdeslTXx1mznObqWhopU1naG8xerAvf7siidGD/Uy+3so9BQjguklqW1JRFMuKDfIkPsSL7w6XOXQituZAEQCXjfnlXNukCF8+uXsq//k+mze2nuB/W3I4b/ggXrp+HO4ujrWYcrYVsfuBd4EW48dnIgGTEzEhxH+AxcBcKWVOl6dKjf8bChR0+XwIv1wl+4Xqk23sOF7JtCGGnl9r9hdT2djGkhk//yYcPdiP0YP9+PNlI9BLw5BiRenO7dNjcXN2Yn1GKZeOCWN0hB8t7TqeWZ/JwmXbWTwxilHGo+JCwPQhQd2+I2vX6VmZWsCchBCH3yZQFMUxXDByEK9tyaG2qc0hJ1tIKVm9v5iJMf5n/LmZGOrD/25OprSuhQ9S8nhp4zHe3JbDffPirRxt35wxC5FSxnb3cV8IIV7EkITNkVJmnvb0CQzJ2PkYTlYihHADZgK/O9e1XZw0/N+XGaz7zSycnQRvbMthRJgPU4cEdvt6VYivmOL6SVFcf9oq1vkjB/Gf77N5d0cuH+3+6fMuWg2/njOEu2cP+dlBgR+OlFHR0MoNajVMURQruWBEKMs2HWdjZjlXOeBM28zSBo6WN/LXK5LO+dpQXzceviCBrNIGXtuSw/WTogh0oPYWVitWEUIsA24HrgdqhBChxocXgDS0PH8B+IMQ4iohRBLwDtAIfHiu64f7uXO84iTLt+bw49FKsssaT/WQUhRz8nFz5onLRrLvzxeQ8vh8Uh6fzw8Pz+aCEYN4YcNRLnzhR9ZnlKLTG3bUP0jJJ8zXjTkJwTaOXFGUgWJUhC+hPm58e6j03C+2Q6v3F6PVCC4ZFWby1zx6YSLN7Tpe2njMgpGZn8nLQkKIK4CHgBHGTx0BnpdSfmHiJX5t/N8fTvv8U8CTxo//CbgDy/ipoesFpvQQ83bTMi8plP/+cJRhg7wJ8Xbl0tG/3FdWFHPxdXfG1zhSaRDw8g3juW5iBX9efYi7V6QR7uvG5WMj2Hq0kgfPi1dF+oqiWI1GI7hg5CA+SS2guU3nUHVTer3kqwPFzIwP6tEp+KEhXlw3MZIVu/K4bVoMMQ7SZ8zUod8PAysxNFl91PjIBD4UQjxiyjWklOIMjye7vEZKKZ+UUoZJKd2klLO7dt4/lz9fNgKtRpBeVMet02LM1nZAUUw1Mz6Y7347i1dvHE9csBevbTmOk0Zw3cRIW4emKMoAc8GIUFra9Ww7VmnrUHpkX0EtRbXNXD6254spD54Xj4tWw3PfZlkgMsswdUXsEeA+KeXyLp97SwixG0OH/H+ZPbJeCPN15w8XD+elH45yo2qaqdiIs5OGi0aFcdGoME5UnqSmqY0wX1WkryiKdU2OC8DbTct3h0o5f4TjjDvalFmOk0YwL6HnMYd4u7FkZhz//eEovyqs7dHpdlsxdcnIC9jUzec3GZ+zGzdPiWb3H89zyFMiSv8TG+TJ+KiBOTReURTbcnbSMD8xhA1Hyugwtt9xBJuyypkQ5Y+vh/O5X9yNpbPi8HLV8s72XPMGZiGmJmJfAtd08/mrgTXmC0dRFEVRFHO5YGQoNU3tpObVnPvFdqCsvoVDxfXMSez94SYvVy1Xjovg6/QSak62mTE6yzhjIiaEeKjzgWGu5B+EEN8KIZ40PtYDj2GoG1MURVEUxc7MHhaMi1bDd4fO2Y7TLmzJqgBgbkLfRsHdNCWatg49q9IKzRGWRZ1tRez+Lo/bMMyEHIZhEPetQILxc7dZNEJFURRFUXrF01XLzKFBfHuoFEOXKPu2KaucUB83EkO9+3SdhFBvJsb480FKHnq9ff93nzERk1LGmviIs2bAiqIoiqKY7oKRgyiqbeZwSb2tQzmrdp2erUcrmZsYbJYeoDdOjia3qontx+371Kjq76AoiqIo/dj84YMQArvYnixvaKGpraPb51Jza2hs7WBOH7clO100KpQATxdW7Mozy/UspScNXa8E5mKY/fizBE5Kea2Z41IURVEUxQyCvFxJjvbn20Ol/Pb8YTaLQ0rJNa/uxEkjWLl0CiE+bj97fnN2Oc5OgulDg8xyP1etE4uSB/PG1hOU1rUQ6ut27i+yAVMbuv4bQ0PXUcZP6U57KIqiKIpipy5MCiOztIETlSdtFkNhTTP51U2cqDzJ9ct3UdHQ+rPnN2dWMCk2AC9X882CvnFSNDq9ZFVagdmuaW6mbk3eCiySUs6VUi6WUl7f9WHJABVFURRF6ZsLk0IBWJdRYrMY9uYbWmg8cdkIimtbuGH5LiobDclYUW0zWWUNzBlmnm3JTlGBHiRF+LDjeJVZr2tOpqadTRhGGimKoiiK4mAi/NwZE+nH+oxSfj1nqE1i2Jdfi7uzEzdPiSYx1Ifb39lN8t82/Ow1c/vQP+xMkqMDWLmngHadHmc7nPlraiL2DPCoEOJXUsruq+wURVEURbFbFyeF8vS6TAqqm4gM8LD6/ffm1zAm0hetk4apQwJZuXQqP2SWn3o+3NeNoSF9a1vRnYkxAbyzI5fDxfWMibS/kUemJmLLgUuBIiFENtDe9Ukp5TxzB6YoiqIoivlclBTG0+sy2XCkjNunx1r13i3tOg4X17N01k8dr8ZE+lklMUqOMYyZ25Nb7dCJ2GvATGA9UAbYd3c0RVEURVF+JirQAz8PZ46VN1r93gcL6+jQS5vM3h3k40ZUgAepuTUsmWn125+TqYnYtcBVUsrvLRmMoiiKoiiWExXgQUFNs9Xv21moPy7KNitSydH+/Hi0AimlWZrFmpOpVWuVQJElA1EURVEUxbIi/T0orG6y+n335tUQE+hBoJer1e8NkBwTQGVjG7lV1v9vPxdTE7EngL8IIbwsGYyiKIqiKJYzOMCdwppmq85flFKyN7/GJtuSnSZ2qROzN6YmYr8DFgBlQogjQoiDXR8WjE9RFEVRFDOJ9PegTaen/LRmqpZUUN1MZWMb46Jtl4gNCfbCz8OZtNwam8VwJqbWiK2yaBSKoiiKolhcZ9uKgpomq4386awPm2DDFTGNRpAc7c+ePPtbETMpEZNSPmXpQBRFURRFsaxIf3cACqqbmBgTYJV77s2vwdPFiYRQ8/cI64nkmAA2HCmnqrHVZrVq3bG/FrOKoiiKolhEhL87QkC+FQv2DY1c/XDS2Pa0YrJxazQ1z762J01aERNCuAB/BK4HogDnrs9LKZ3MH5qiKIqiKObkqnVikLcbBdWWa2FxoKCW3392kMZWwyCeotpm7rXRWKWuRg32xUWrITW3mgUjQ20dzimmroj9FcPg738DegzF+8uAKuDXpt5MCDFLCLFGCFEkhJBCiNtOe/4d4+e7PnaZen1FURRFUc4uMsCdghrLrIg1tnbwwMf7qG1qZ1JsAJNiA7h2QiTXJkda5H494ap1YsxgX3bl2FedWE8aut4tpVwvhPgXsFpKeVwIcQQ4H/ifidfxAjKA94yP7mwAbu7y5zYTr60oiqIoyjlE+nuwK6fKItf+61eHya9uYuXSqUyKtU4NWk/Mig/m399nU9HQSrC3fdSJmboiNgg4bPy4EehsjbseuMDUm0kp10opH5dSrsKwstadVillaZeHfaWuiqIoiuLABgd4UFLfQlvHmX4N9863h0pZmVrAPbOH2GUSBjA3MQSAH7MrbBzJT0xNxPKBcOPHxzD0FAOYCph7o3mGEKJcCJEthFguhAg502sUvEUAABYpSURBVAuFEEuFEKlCiNSKCvv5S1UURVEUexXp746UUFxrvl/f5Q0tPPZ5OkkRPjx43jCzXdfcRoT5EOztyqascluHcoqpidgXwHzjxy8CTwkhTgDvAG+YMZ71wC3Gez0MTAI2CiG6XT+UUr4upUyWUiYHBwebMQxFURRF6Z+69hIzl79/c4STrR28cN1YXLT225BBoxHMGRbMj9kVdOjMuyLYW6b2EXusy8erhBAFwHQgW0r5tbmCkVJ+3OWP6UKINCAPuAT43Fz3URRFUZSB6lQiZqaTk+mFdazeX8y9c4cwNMS2vcJMMTcxhE/TCtlXUGu1Xmpn06u0VUqZIqV8/vQkTAjxjRAizDyhgZSyGCgE4s11TUVRFEUZyEJ93HB2EmZZEZNS8vS6IwR4uvCr2UPMEJ3lzYgPwkkj2JRpH9uT5l4/nAW4m+tiQoggIAIoMdc1FUVRFGUgc9IIwv3cKTBDU9ct2RXsOF7F/fOG4uPmfO4vsAM+bs4kR/uzKcs+asutupErhPASQowVQow13jvK+Oco43P/EkJMFULECCHmAF8B5Rhq1BRFURRFMYOoAA8Kavq2NanTS55Zl0lUgAc3To42U2TWMTcxhCMl9ZTWtdg6FKuPOEoG9hkf7sBTxo//AuiAUcBqIBt4F8gCpkopG6wcp6IoiqL0W4P9PSjs44rY53sLySxt4NELE+y6QL87cxMMDRk228HpSVMbupqFlHIzcLZhUwvO8pyiKIqiKGYQGeBO1ck2TrZ24Onau1Tg4z0FJIZ6c8kos5WGW82wQV6E+7qxOauCxZOibBqLY6WwiqIoiqL0WaR/31pY6PWSzJJ6psQFIoRth3n3hhCCOYkhbD1aQUu7zqaxqERMURRFUQaYvrawKKhp4mSbjsRQ+29XcSYXJYVysk1n8+3JsyZiQoi3hBA9+Vv+B6BGEimKoiiKHYv0NzQ46O3JySMlhtLt4WE+ZovJ2qbGBRLk5crq/cU2jeNcK2K30oN2FFLKp6WUtX0LSVEURVEUSwrwdMHDxYlDxfUcK2/gWHkDtU1tJn/9kZJ6NAKGDXLcFTGtk4ZLR4fxQ2Y59S3tNovjXImY4238KoqiKIpyVkIIYoM8+WxvIec9/yPnPf8jF/znR5PrpY6U1BMT5Im7i5OFI7Wsy8eG09ah59uMUpvFYMpRCWnxKBRFURRFsaqXrh/HoeJ6AIpqm3lmXSZf7isy6RRhZmkDowb7WjpEixsX6UdkgPv/t3fn8VaV9R7HP18QBGTSHEDB0hyQAM2BtBwQ5aqp2eDUYJpFdTXr9rKrt+z20rLSBsur10rNHHLIe+1qDpGaQyI5K044A06gghkgoCK/+8fzHNlsz9lnA3vttc/h+3699usc1nr2WT/Wep5n/9Z6nrUXf5r6IgdtP7yUGOqZrD9b0tu1XoVHaWZmZg216Xr92X/rDdl/6w35yq6bMnLoQM6dPJ2IZddf3lyylHNve4Z/Llw2dDd/8Vs8++pCturCE/XbSOKArTfi9qfm8Mr8N0qJoZ5E7MvAwZ28zMzMrIuSxJd22YSnXl7ArU8se/TPaTc8wcnXTuP3d858Z9kTL3X9ifqVPrbNhiwNuPbBd0/aX7o0OPqS+7hgyozCtl9PInZ1RFxR61VYdGZmZtYU+43ZkA0GrslvJ08H4I5n5vKbvz2NBNc9tOyRz4/mOyZHdJNEbIsNBjBiyACumvruROyyu5/j2gdncd7ty18pbKTOEjHPDzMzM1sN9F6jB4d/+H3c9uQc7pr+KsdePpX3rtOPb+yxOY+8OI9n56avunhs1jwG9lmDDQf1KTnixjlgm424/9nXuPOZue8sm7PgDU6d9Bhr9e7JzLkLeerlBYVs23dNmpmZGQCfGbsxfXv15PDz7mL2vMX84pBt+NS2wwD488Ppqti0WfMYMXRgl/xG/Y4cusNwNl13LY48/27ue/YfAPzoumksfHMJZ39+ewBumPZSIduumYhFRI+IKP+JmGZmZla4wf16c9D2w1j01tscM34zPrjx2gxfpx9jhg3iuodns3Rp8Pjs+YzsJsOSbdZeqzeXTNyRdQesyeG/vYtzb3uGP973AhN32ZSPbLYuY4YN4oZHS0jEzMzMbPVy7IQtOeWTo/na7pu9s2zvUUOY+txr3DF9bpd/tFFHhgzqw6UTd2TwWr04+dppDFu7L8eM3xyAPbfagAeee42X5y9u+HadiJmZmdk7BvXrxaFjN2aNnstShH1GDQXglzc8CXSfOyarbTi4L5dO3JFdt1iPnxw45p0vrJ0wcgMi4KZpjR8krOcLXc3MzGw1tsm6a7HV0IHcNePVLv9oo84MW7sfFx45drllI4YMYKPBfbmxgHliviJmZmZmnfroqCEA3eLRRitKEhNGbsBtT85p+N92ImZmZmad2md0Gp7srsOSnfnUtsM4fu8RDf+7Hpo0MzOzTm22fn+O/Mgm7LblemWHUorRwwYxetggvtjgv+tEzMzMzOryvf1Hlh1Ct+OhSTMzM7OSOBEzMzMzK4kTMTMzM7OSOBEzMzMzK4kTMTMzM7OSKCLKjqEhJM0HHi87jg6sCzT+W+BWXavGBa0bW6vGBY5tZbRqXNC6sbVqXNC6sbVqXODYVsaWEdGwRwt0p6+veDwiti87iPZIuqcVY2vVuKB1Y2vVuMCxrYxWjQtaN7ZWjQtaN7ZWjQsc28qQdE8j/56HJs3MzMxK4kTMzMzMrCTdKRE7u+wAamjV2Fo1Lmjd2Fo1LnBsK6NV44LWja1V44LWja1V4wLHtjIaGle3maxvZmZm1tV0pytiZmZmZl2KEzEzMzOzkjgRMzMzMytJt0jEJB0labqkxZLulbRLk7e/q6Q/SXpBUkg6omJdL0mnSnpQ0uuSZkm6RNLGTYjr25LuljRP0iuSrpY0qkb5s3P832pCbEfnfTIvv/4uad+qMltI+qOk1yQtlHSfpK2Kjq0qhu/kfXJmxbL+ks6Q9LykRZIel/TNJsUzVNIF+XgulvSopN0q1kvSiZJezLHdIukDTYhrRt5P1a9r8/oVqosNjKunpB9U9A/TJZ0sqd3vUGxmG8jbGyDpl5Jm5uM1RdIOVWUKbwe1+rC8vtN6leO8UtIcSfMl3SFp76LiqrdvlbRmbq9zcrk/SRq2KnF1Flte/wNJj+Vt/kPSXyV9uGL9Ojmux/I+fU7SryS9p8i4qsrWrO/5uE/KZQ5clbjqiU3S+e30IXe083fGSrpB0oJc16ZIWreouHKZmu1Q0hBJF0manY/5VEmfrWf7XT4Rk3QIcDrwI+CDwBTgz9WNsWD9gYeBbwCLqtb1A7YFfph/HgAMByZ19GHQQOOAs4APA+OBJcCNktapLpgb2Q7AiwXH1OZ54HjSPtkeuAm4UtKYHM8mwO3AdFLso4DvAguaFB+SdgQmAg9WrToN2Bc4DNiKdGxPkXRYwfEMJu0T5e1vBRwDvFxR7Djg2Lx8h7zuBkkN+xboDuwADK14bQsEcHleP44662KDHQ8cDXwdGEFqo0cD364uWEIbADgX2As4HBgNXE/aLxvlmJrVDmr1YVBfvboG6APsQeqLJwNXSXp/QXHV27f+EvgU8GlgF2AgcI2knqsQV2exQXrSy9Gk47oz6RhOkrRBXr8hsBFp344GPgfsClxacFxA3fX9WODtVYxnRWO7keX7ko9WrpT0IVI7uQXYEdgO+BnwVlFx1dkOLyT1yQeQjueFwEWSdu106xHRpV/AncA5VcueBH5cUjwLgCM6KTOS9CE1usmx9Sc1qv2rlr8XeCFXohnAt0rad68CX8m/XwJcXEYcefuDgKdzo7sFOLNi3cPASVXlb60sU1BMPwJur7FewCzghIplfYH5bfu1ifvvBOA1oF8H69utiwXEcQ1wQdWyC4BrqpY1vQ3kY7MEOKBq+b3Ayfn3preD6j6snnpFehRNALtXlFkjH+MDi4irgzLL9a25Hb8JfLaizHBgKbBXUfusgzIDc2wdbpeUdCwFBhYZVz31nXSC/Bywfo67IcexVmzA+dVts533TQF+2MhY6oir03aY3/eFqmUz6+lLuvQVMUm9Sdnw9VWrriedebeqgfnnP5q83QGkq6DvbDefOV5K6vinNTmethh6SjqU9OE8RVIPYH/g0XxZ/BWlYa1DmhjW2cD/RsRN7aybDOwvaThAHm7YBphUcEwfB+6U9AdJL0t6QNLXJCmv3wQYQkV7iIhFwN9oYnvI8XwR+H1ELOyg2LvqYkEmA7tLGpFjG0lKrq9rK1BiG1gD6Aksrlq+CNi5RdoB1Fev5gLTgMOUhu57Al8mJWu3NzHW6r51O6AXy8f+XI61mW2iN2l/zAMeqFF0IPAG0FG7aUQsndb3fKXzUlKi/XJ7ZQq0c+7fnpB0jqT1K+JaH9gJmCVpsqSXJN0maY+iglmBdjgZOFjSeyT1kHQAsB7pCl9NXToRI52F9QReqlr+EqnjaDm5Qf4cuDoinm/y5k8ndQJ/r1h2EjA3In7V5FiQNFrSAlLH82vgExHxEOkMrD/wHVIHOoHUKVwsab8mxDUR2Az4zw6KfJ20H5+V9BbpatjxEXFNwaFtChwFPEMazjodOIU0/AHL6nzZ7WEC6cP73Bpl2quLRTgVuIjUib4FPEK6QnZWRZlS2kBEzCf9/78raaN8QvI50gfNUEpuBxU6rVeRTv8nkIZs5pHa9InAPhExqxlBdtC3DiFdlat+cHRT2oSk/XIftxj4JjAhIqr3Y1vZwcAPSCM8SwoMq576/mtgUkRcV6NMESYBnycNbx8LjAVukrRmXr9p/nkScB6wN3Ab8BdJWxcUU73t8GDSlcM5pPp/MfDpiKiVeAPd56Hf1d9Kq3aWlS6fifweGAx8rMnbPo00T2HniHg7L9sNOIJ0NacMj+dtDybN4bhA0jjSECXAVRFxWv79AUnbk5KOwhIeSVuShgB3iYg3Oyh2DPAR0jGcSZrX8TNJMyKiyKtiPYB7IqJtftP9kjYn7ZMzK8qV3R4mAnd31AG1VxcLdAipY/8MKQnbBjhd0vSI+G0LtIHDSB8oz5MShvtInfy2LDtRbno76ECH9SpfBT2LdGVsF9JVvS8BV0jaISJeKDKwlehbm9UmbibVrXVJ7eJySTtVJ6eS1gKuJg0XHldUMPXU9zzXdWvS0GRTRcRlFf98SNK9pD52X+CPLGsTv4mI8/Lv9+fPja8C/1pAWPW2w5NJx3lPUjL2ceBCSbtGxNR6NtBVzSF1XtVnNuvz7rO3UlVcDh4D7BERc5u47V+QJqqOj4hnKlbtTjrzniVpiaQlpLkDp0oq/GpdRLwZEU9FRFty8QDprHEOae7Mo1VvmQYUfRPGTqTG9HDFPtkNOCr/exDwY+C4iLg6Ih6MiDOBy4Ci77SbRe19Mjv/LK095KGDA4BzOljfUV0syk+Bn0XEZRHxUERcRLrZoi2ZLbsNPB0Ru5HOuIdHxFjSUNp0ym0HleqpV+NJwzefjojbI+K+iDgKeB34QpHBddK3ziaNmlTfUdeUNhERr+c+7o6I+CJpQvmXKstI6g/8Of9zv4ioHqpupHrq+x6kuXYLKsoA/EHS5AJje5eIeJF0krJ5XtSWwDazTXTaDvMNKccAEyPirxExNSJOAu7Oy2vq0olYvmJxL+lSYaUJpAl9LUFSL+APpI5i94iY3clbGrnt00lXA8ZHxGNVq8/KMW1T8XoR+AWpMTZbD2DNfFzvBrasWr8F6eyoSFeS7nip3Cf3kBKttrPIXrz7TqK3Kb493U7tfTKd9MHzTnuQ1Id0haJZ7eELpMvyl1Wv6KQuFqUftY9VS7SB/IE9S9LapGHnq0puB5XqqVf98s+lVe9dSoHtoo6+9V5S8lMZ+zDSJPUyPiN6AG3DbG1zsSaRksWPRkTRd4XXU99PaKcMpBPNzxcc33KUvpJiI5YlYDNI8TatTdTZDtvq/8p9LhR150GzXqShhzdJZxlbkeaeLADe28QY+rOswi4Evpd/35g0/Hsl6ZLztqSzyrZX34Lj+m/SfI3xVdvtX+M9M2jOHWOnkDry95ESnx+TOu198vqP5+P6ZdJ8rYmkDnXfEurYLSx/1+QtpDsnx5HmQh1BGoo5puA4dsj74IS8Tw4C/gkcXVHm+HzMP0mar3MZqeMa0IT9JNIdy+e0s26F62KDYjqfdEa9b65rnwBeAX5e4z1NaQN5W3sB++R6NIF0VfhOoFde35R2UKsPq6deka44zQGuIA1rbUG6GvkWsG0RcVFn3wr8KpfZk/S1Gjfn/dyzqH1GmnR/MvCh/O/tSEPQbwBj8vsHkOYIPkK64lMZf++ijuXK1HcadNdkJ/usP+lrKHbKbXVc3j/PU9F/Af9G6vcOym3iO7mebV1g/a/ZDkkn50+SbmAZC7yfNMdtKXXcGV5oJ9OsF2kC84xcye8Fdm3y9sflilr9Oj9XqPbWBZ3c7tyAuDra7ok13tNpo2xQbOeTzibeIH0n0Y1U3dZNSnCeICU5D5KGPcqoX7ewfCI2BPgdqXNfBDxGOltUE2LZF5hKmvz7BOnGAVWsF2mS9Kxc5lZgVJP20+65fo1tRF1sUEwDSN8jNTMfq2dIcwD71HhPU9pA3tbBpK9JeSMfszOBQVVlCm8HtfqweusVaU7RX0jzxOaREspVShhrxUWdfSvpu83OyHEtJM3FGl7kPiNdIfk/UrL6Rv55FfChOt4fwLiijuXK1Hcal4jV2md9c/15mZT0zMzL33WsSPPoniUNfd8F7Flk/a+nHZKS6StIQ96vk/rpw+vZvvIfMDMzM7Mm69JzxMzMzMy6MidiZmZmZiVxImZmZmZWEidiZmZmZiVxImZmZmZWEidiZmZmZiVxImZmXZakkHRg2XEASJohqejHXJlZN+NEzMxaTk6war3Oz0WHkr6g08ysS1qj7ADMzNoxtOL3/UgPEa9ctgggmvjcVjOzIviKmJm1nIiY3fYCXqteFhH/hOWHJiW9L//7UEm3Slok6X5JYySNkjRF0uuSJkvapHJ7kvaXdK+kxZKmS/qhpN4rEXofSb+RNE/S85L+fVX3hZl1b07EzKy7OQk4lfSA59eAS0jPGjyB9EDePsB/tRWWtBdwMek5jx8AjgQOJD2TckV9E3iI9BDqU4GfSNppZf8jZtb9OREzs+7mtIi4LiIeA35OSq7OiIibI+IRUsK1e0X5E4CfRsTvIuLpiLgZOB74qiSt4Lavj4gzI+KpiDgDeArYY9X/S2bWXXmOmJl1Nw9W/P5S/vlQ1bK1JPWLiIXAdsBYScdXlOkB9AWGALNWctsALwLrr8D7zWw140TMzLqbtyp+jxrLelT8PAn4n3b+1iursO22bXnkwcw65ETMzFZ39wEjIuKpsgMxs9WPEzEzW919H7hG0kzgcmAJMAoYGxHHlRqZmXV7vmRuZqu1iPgLsC9pAv9d+fUfwLNtZSSdKCna/wtmZitPEe5bzMxqkXQBMDQi/qXsWMyse/HQpJlZDfkrLMbjr6EwswL4ipiZmZlZSTxHzMzMzKwkTsTMzMzMSuJEzMzMzKwkTsTMzMzMSuJEzMzMzKwkTsTMzMzMSvL/juSUJN3QSCAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf664960b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf664eb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End reward: -162.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf664b3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('RL Agent')\n",
    "# show_results(agent, perturbation_mode, reward_limit=-5000., save_prefix=\"RLCool\") # plot_T=True)\n",
    "show_results(agent, perturbation_mode, reward_limit=-5000., save_prefix=\"RLCool\", plot_T=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}